Design and implementation of students timetable management system

TITLE PAGE
DESIGN AND IMPLEMENTATION OF MOBILE BASED
STUDENTS TIMETABLE MANAGEMENT SYSTEM
(A CASE STUDY OF DEPARTMENT OF COMPUTER SCIENCE
AKANU IBIAM FEDERAL POLYTECHNIC UNWANA, AFIKPO,
EBONYI STATE)
BY
ONUWA, NNACHI ISAAC
2013/HND/15268/CS
SUBMITTED TO
DEPARTMENT OF COMPUTER SCIENCE
SCHOOL OF SCIENCE
AKANU IBIAM FEDERAL POLYTECHNIC, UNWANA, AFIKPO,
EBONYI STATE.
IN PARTIAL FULFILMENT FOR THE AWARD OF HIGHER
NATIONAL DIPLOMA (HND) IN COMPUTER SCIENCE
DECEMBER 2015

CERTIFICATION / APPROVAL PAGE
This is to certify that this project work is carried out by ONUWA, NNACHI
ISAAC (2013/HND/15268/CS) has been scrutinized and approved by the
undersigned as part of the requirements for the award of the Higher National
Diploma in Computer Science, Akanu Ibiam Federal Polytechnic Unwana.
………………………………… ………………………….………
MR. J. O IDEMUDIA DATE
PROJECT SUPERVISOR
………………………………… ………………………….………
DR. A. N. EZEANO DATE
HEAD OF DEPARTMENT
………………………………… ………………………….………
EXTERNAL SUPERVISOR DATE

ACKNOWLEDGMENT
My profound gratitude goes to Jehovah the creator of the universe.
This work is a synergistic product of many minds and I feel a deep sense of
gratitude to my parents, Mr and Mrs Onuwa U. Onuwa for their encouragement
and for being ever supportive. My sincere thanks goes to my supervisor Mr J. O.
Idemudia for his thorough assistance with this work and to Mr Chibuike
Madubike and Mr Okwara K. K for their encouragement and advice.
I appreciate the effort and care of the Head of Computer Science Department Dr.
A. N. Ezeano (Ph.D.). My thanks goes to Chief Mrs. V. N. Ezeano (J.P), Mr E.
U. Ezeorah and Mr U. C. Ugboajah. I also acknowledge the Computer Science
Students (set 2013/2014) for their active verbal participation and suggestions
towards the evolvement of this project work.
I am greatly indebted to my Uncle, Mr Arua Smart and my Port Harcourt parents
Mr and Mrs Moses Ikekwem who did not count on the hard economy but tried
their best to send me to school and supported me both financially, materially and
morally. I cannot as well fail to acknowledge my siblings – Chinedu, Nnamdi and
Onyinyechi, my aunties Aunty Chidinma, Aunty Chinyere and Aunty Nkechi, my
cousins Chinyere Ikekwem, Obinna Ikekwem, Helen Ude, Ukachukwu Ukpai.
I love you all.

ABSTRACT
Lecture timetabling is a very important process in any educational institution. It
is an open-ended program in which courses must be arranged around a set of time
slot ’T’ and remains so that some constraints are satisfied. It constitutes a class of
difficult-to-solve optimization problems that lacks analytical solution method.
Data gathering on the current system was analysed to create a requirement
definition for the improved timetable system. Literature review was carried out
to search the best approach that can help to solve the problem in the timetable
system. Genetic Algorithm has been implemented in the Timetable Management
System. This is because Genetic Algorithm is able to produce a feasible timetable
system. Java, XML and PHP programming languages were used in developing
the solution. MySQL database was used as the back-end for the solution. The
front-end solution will be implemented in an android mobile operating system for
easier accessibility and proximity to users.

TABLE OF CONTENTS
TITLE PAGE …………………………………………………………………..………. i
APPROVAL PAGE ………………………………………………………..................... ii
DEDICATION ……………………………………………………………..................... iii
ACKNOWLEDGEMENT ……………………………………………………………... iv
ABSTRACT …………………………………………………………………………..... v
TABLE OF CONTENT ………………………………………………………………...vi
CHAPTER ONE: INTRODUCTION
1.1 BACKGROUND OF THE STUDY ……………………………………..3
1.2 STATEMENT OF THE PROBLEM …………………………………….4
1.3 OBJECTIVES OF THE STUDY ………………………………………...4
1.4 SIGNIFICANCE OF THE STUDY ……………………………………...5
1.5 SCOPE OF THE STUDY ………………………………………………..6
1.6 LIMITATION OF THE STUDY ………………………………………...6
1.7 DEFINITION OF TERMS ……………………………………………….6
CHAPTER TWO: REVIEW OF RELATED LITERATURE
2.1 REVIEW OF RELEVANT THEORIES AND TECHNOLOGIES ……...9
2.2 TIMETABLING AS A NP-COMPLETE PROBLEM ………………….14
2.3 BRIEF HISTORY OF GENETIC ALGORITHMS ……………………..15
2.4 BASIS FOR A GENETIC ALGORITHM ………………………………19
2.5 METHODS OF REPRESENTATION …………………………………..21
2.6 METHODS OF SELECTION …………………………………………...23
2.7 METHODS OF CHANGE ………………………………………………26
2.8 STRENGTHS OF GENETIC ALGORITHMS ………………………….27
2.9 LIMITATIONS OF GENETIC ALGORITHMS ………………………..35
CHAPTER THREE: SYSTEMS INVESTIGATION AND ANALYSIS
3.1 ORGANOGRAM FOR COMPUTER SCIENCE DEPARTMENT …….. 46
3.2 FACTS FINDING ………………………………………………………...48
3.3 ANALYSIS ……………………………………………………………….48
3.4 PROBLEM OF THE CURRENT SYSTEM ……………………………...49
3.5 PROPOSING A NEW SYSTEM …………………………………………50
3.6 ADVANTAGES OF THE PROPOSED SYSTEM ……………………….50
CHAPTER FOUR: SYSTEM DESIGN
4.1 OBJECTIVES OF THE DESIGN …………………………………………52
4.2 SYSTEM BLOCK DIAGRAM …………………………………………….53
4.3 OUTPUT DESIGN …………………………………………………………54
4.4 INPUT DESIGN ……………………………………………………………54
4.5 PROGRAM DESIGN ………………………………………………………54
4.6 DATABASE ………………………………………………………………..57
4.7 DATABASE SPECIFICATION …………………………………………...57
4.8 PROGRAM FLOWCHART ……………………………………………….58
4.9 MODELLING THE SYSTEM …………………………………………….59
4.10 CHOICE OF PROGRAMMING LANGUAGE …………………………...66

CHAPTER FIVE: SYSTEM DOCUMENTATION AND IMPLEMENTATION
5.1 SYSTEM REQUIREMENTS ………………………………………………67
5.2 HOW TO INSTALL ………………………………………………………..68
5.3 TRAINING OF OPERATORS ……………………………………………..68
5.4 IMPLEMENTATION METHOD …………………………………………..68
5.5 REVIEW AND MAINTENANCE OF THE SYSTEM …………………….70
CHAPTER SIX: CONCLUSION, SUMMARY AND RECOMMENDATION
6.1 SUMMARY ……………………………………………………………………...72
6.2 PROBLEMS ENCOUNTERED ………………………………………………….72
6.3 CONCLUSION …………………………………………………………………...72
6.4 CONTRIBUTION TO KNOWLEDGE …………………………………………..72
6.5 RECOMMENDATION …………………………………………………………...73
REFERENCES ……………………………………………………………………………74
APPENDICES …………………………………………………………………………….78

CHAPTER ONE
INTRODUCTION
Timetabling concerns all activities with regard to producing a schedule that must
be subjective to different constraints. Timetable can be defined as the
optimization of given activities, actions or events to a set of objects in space-time
matrix to satisfy a set of desirable constraints.
A key factor in running an educational center or basically an academic
environment is the need for a well-planned, well-throughout and clash-free
timetable. Back in the days when technology was not in wide use, (lecture)
timetables were manually created by the academic institution.
Every school year, tertiary institutions are faced with the tedious task of drawing
up academic timetables that satisfies the various courses and the respective
examination being offered by the different departments.
Timetable development process starts when each Head of Department provide
the following information to be used for timetable scheduling. The information
provides the modules with dates, time and venues suitable in a particular
semester:
 Examinable courses in a particular semester.
 Dates for lectures to be held (Lectures can be scheduled between Monday
and Friday).

 Specified time for lectures ( i.e. Between 8am and 4pm)
 The venue of the scheduled lectures.
A timetabling problem consists of four (4) parameters and they are: T (set of
time), R (set of available resources), M (set of scheduled contacts) and C (set of
constraints). This problem assigns time and resources to the contacts on such a
way that the constraints will be satisfied. In various timetabling problems,
educational timetabling has been generally examined from practical standpoint.
Academic timetable is very crucial but it consumes time due to its frequent
occurrences and usage among higher institution of learning. Another reason for
the difficulty is because of the great complexity of the construction of size of
lectures and examinations, due to the scheduling size of the lectures and
examinations periods and high number of constraints and criteria of allocation
which is usually circumvented with the use of little strict heuristics, based on
solutions from previous year (Jose, 2008).
The quality of the timetable determines the quality of time dedicated by lecturers,
students and administrators to academic activities. Various academic timetabling
includes:
i. School timetable
ii. Lecture timetable
iii. Examination timetable and
iv. Assignment timetable (Qu, Burke, McCollum, Merlot and Lee, 2004).

This academic timetable must meet a number of requirements and should satisfy
the desires of all entities involved simultaneously as wee as possible. The timings
of events must be such that nobody has more than one event at the same time
(Roberts, 2002).
1.1 BACKGROUND OF THE STUDY
The Department of Computer Science was carved out from the defunct Systems
Science (Two departments were created out of Systems Sciences. Computer
Science and Mathematics/Statistics) in the year 1997 with Mr. C.J.C. Ayatalumo
as her first Head of Department.
The department’s mission and vision are as follows
 To procure department portal CNET.
 To make the Hardware Maintenance Laboratory fully functional.
 To undertake repairs, maintenance, installation, assembling of computers
within and outside the institution in order to generate revenue for the
polytechnic.
 To start off new Computer Engineering Technology Department.
 To become Center of Excellence in Computer Science and Engineering.
 To resuscitate staff development programmer in the department.
 To increase the computing equipment in use in both ND and HND
laboratories.
 To ensure regular supply of consumables.

 Procurement of up-to-date software (licensed).
 To provide adequate staff offices and facilities.
 Provision of dedicated power supply.
The department of Computer Science is in the School of Sciences and has been
accredited to award National Diploma (ND) and Higher National Diploma
(HND).
1.2 STATEMENT OF THE PROBLEM
The available system currently builds or generates a set of timetables, but most
times have issues with generating a clash-free and complete timetable. The
tedious tasks of data introduction and revision of usually incomplete solutions are
the bottlenecks in this case (Luisa et.al, 2006). Most educational institutions have
resorted to manual generation of their timetables which according to statistics
takes much time to get completed and optimal. Even at the optimal stage of the
manually generated timetable, there are still a few clashes and it is the lecturer
that takes a clashing course that works out the logistics of the course so as to
avoid the clash.
1.3 AIM AND OBJECTIVES OF THE STUDY
The literature on and implementation of educational timetabling problem is
scattered, vast and far-fetched. Different research papers that have been brought
out on timetable may refer to the same type of institution but they mostly deal

with different kinds of assignments, i.e. decisions like timing of events, sectioning
of students into groups, or assigning events to locations.
Moreover, each institution has its own characteristics which are reflected in the
problem definition (Robertus, 2002). Yet, there have been no leveling ground for
developing a system that can work for most of these institutions.
The aim of this work is to generate a timetable while demonstrating the possibility
of building the schedules automatically through the use of computers in a way
that they are optimal and complete with little or no redundancy.
The objectives of this work are as follows
 To be able to optimize the algorithm used in today’s timetable systems to
generate the best of timetabling data with fewer or no clashes.
 To bring approved timetable closer to users especially to those who use
android phones.
1.4 SIGNIFICANCE OF THE STUDY
The reasons for this work are outlined below
i. The proposed system will provide an attractive graphical front-end for
the administrators and students (mobile platform).
ii. It will improve flexibility in timetable construction.
iii. The system will save time.
iv. Productivity will be improved.

v. The system can be revised i.e. its backend can be revised.
vi. Proper recording of class size, number of courses offered, number and
capacity of available lecture halls.
vii. Efficient execution of academic activities.
1.5 SCOPE OF THE STUDY
This study will only cover the management and allocation of spaces and time for
lectures in the Department of Computer Science, Akanu Ibiam Federal
Polytechnic Unwana.
1.6 LIMITATIONS OF STUDY
The researcher outlined some of the limitations as follows
i. Incomplete data from data collation officers.
ii. Wrong data input from technical user.
iii. Wrong constraint specification.
1.7 DEFINITION OF TERMS
Allocate To set apart for a specific purpose
Android This is a mobile operating system based on the
Linux Kernel and currently developed by
Google.
Backend Application Serves indirectly in support of front-end
services, usually by being closer to the required

resource or having the capability to
communicate with the required resource.
Frontend Application This is an application that users interact with
directly.
Genetic Algorithm GA is a model of machine learning which
derived its behavior from metaphor of the
process (es) of EVOLUTION in natural
sciences.
Google Cloud Messaging GCM is a tool from Google that allows
developers to send data from their server(s) to
users’ device(s) and receive message(s) from
devices on the same connection.
JavaScript Object Notation JSON is a lightweight data-interchange format.
It is easy for human to read and write. It is a
collection of name/value pairs.
Hypertext Preprocessor PHP is a server-side scripting language used in
building dynamic content for the web.
Extensible Markup Language XML is a language used in designing android
layouts.

Timetable This is a table of events arranged according to
the time when they take place.

CHAPTER TWO
REVIEW OF RELATED LITERATURE
A timetable is an organized list, usually set out in tabular form, providing
information about a series of arranged events in particular, the time at which it is
planned these events will take place. They are applicable to any institution where
activities have to be carried out by various individuals in a specified time frame.
From the time schools became organized environments, timetables have been the
framework for all school activities. As a result, schools have devoted time, energy
and human capital to the implementation of nearly optimal timetables which must
be to satisfy all required constraints as specified by participating entities
(Robertus, 2002).
The lecture timetabling problem is a typical scheduling problem that appears to
be a tedious job in every academic institute once or twice a year. The problem
involves the scheduling of classes, students, teachers and rooms at a fixed number
of time-slots, subject to a certain number of constraints. An effective timetable is
crucial for the satisfaction of educational requirements and the efficient
utilization of human and space resources, which make it an optimization problem.
Traditionally, the problem is solved manually by trial and hit method, where a
valid solution is not guaranteed. Even if a valid solution is found, it is likely to
miss far better solutions. These uncertainties have motivated for the scientific
study of the problem, and to develop an automated solution technique for it. The

problem is being studied for last more than four decades, but a general solution
technique for it is yet to be formulated (Datta D. et.al, 2006).
Timetabling problem is one of the hardest problem areas already proven to NP-
complete and it is worthy of note that as educational institutions are challenged
to grow in number and complexity, their resources and events are becoming
harder to schedule (Ossam Chohan, 2009).
2.1 REVIEW OF RELEVANT THEORIEDS AND TECHNOLOGIES
Solutions to timetabling problems have been proposed since the 1980s. Research
in this area is still active as there are several recent related papers in operational
research and artificial intelligence journals. This indicates that there are many
problems in timetabling that need to be solved in view of the availability of more
powerful computing facilities and advancement of information technology (S.B.
Deris et.al, 1997).
The problem was first studied by Gotlieb (1962), who formulated a class-teacher
timetabling problem by considering that each lecture contained one group of
students, one teacher, and any number of times which could be chosen freely.
Since then the problem is being continuously studied using different methods
under different conditions. Initially it was mostly applied to schools (de Gans,
1981; Tripathy, 1984). Since the problem in schools is relatively simple because
of their simple class structures, classical methods, such as linear or integer
programming approaches (Lawrie, 1969; Tripathy, 1984), could be used easily.

However, the gradual consideration of the cases of higher secondary schools and
universities, which contain different types of complicated class-structures, is
increasing the complexity of the problem. As a result, classical methods have
been found inadequate to handle the problem, particularly the huge number of
integer and/or real variables, discrete search space and multiple objective
functions.
This inadequacy of classical methods has drawn the attention of the researchers
towards the heuristic-based non-classical techniques. Worth mentioning non-
classical techniques that are being applied to the problem are Genetic Algorithms
(Alberto Colorni et al., 1992), Neural Network (Looi C., 1992), and Tabu Search
Algorithm (Costa D., 1994). However, compared to other non-classical methods,
the widely used are the genetic/evolutionary algorithms (GAs/EAs). The reason
might be their successful implementation in a wider range of applications. Once
the objectives and constraints are defined, EAs appear to offer the ultimate free
lunch scenario of good solutions by evolving without a problem solving strategy
(Al-Attar A., 1994). A few worth mentioning EAs, used for the school
timetabling problem, are those of Abramson et al. (1992), Piola R.(1994), and
Bufe et al. (2001). Similarly, EAs, used for the university class timetabling
problem, are those of Carrasco et al. (2001), Srinivasan et al. (2002) and Datta et
al...
Since 1995, a large amount of timetabling research has been presented in the
series of international conferences on Practice and Theory of Automated

Timetabling (PATAT). Papers on this research have been published in conference
proceedings, see e.g., (Burke & Carter, 1997) and (Burke & Erben, 2000), and
three volumes of selected papers in the Lecture Notes in Computer Science series,
see (Burke & Ross, 1996), (Burke & Carter, 1998), and (Burke & Erben, 2001).
Additionally, there is a EURO working group on automated timetabling (EURO-
WATT) which meets once a year regularly sends out a digest via e-mail, and
maintains a website with relevant information on timetabling problems, e.g., a
bibliography and several benchmarks.
Fang (1994), in his doctoral thesis, investigates the use of genetic algorithms to
solve a group of timetabling problems. He presents a framework for the utilization
of genetic algorithms in solving of timetabling problems in the context of learning
institutions. This framework has the following important points, which give you
considerable flexibility: a declaration of the specific constraints of the problem
and use of a function for evaluation of the solutions, advising the use of a genetic
algorithm, since it is independent of the problem, for its resolution.
Gröbner (1997) presents an approach to generalize all the timetabling problems,
describing the basic structure of this problem. Gröbner proposes a generic
language that can be used to describe timetabling problems and its constraints.
Chan (1997) discusses the implementation of two genetic algorithms used to
solve class-teacher timetabling problem for small schools.
Oliveira (Oliveira and Reis, 2000) presents a language for representation of the
timetabling problem, the UniLang. UniLang intends to be a standard suitable as

input language for any timetabling system. It enables a clear and natural
representation of data, constraints, quality measures and solutions for different
timetabling (as well as related) problems, such as school timetabling, university
timetabling and examination scheduling.
Fernandes (2002) classified the constraints of class-teacher timetabling problem
in constraints strong and weak. Violations to strong constraints (such as schedule
a teacher in two classes at the same time) result in an invalid timetable. Violations
to weak constraints result in valid timetable, but affect the quality of the solution
(for example, the preference of teachers for certain hours). The proposed
algorithm, evolutionary, has been tested in a university comprising 109 teachers,
37 rooms, 1131 a time interval of one hour each and 472 classes. The algorithm
proposed in resolving the scheduling without violating the strong constraints in
30% of executions.
Eley (2006) in PATAT'06 presents a solution to the exam timetable problem,
formulating it as a problem of combinatorial optimization, using algorithms Ant,
to solve.
Analyzing the results obtained by the various works published, we can say what
the automatic generation of schedules is capable of achieving. Some works show
that when compared with the schedules manuals in institutions of learning real,
the times obtained by the algorithms for solving the class-teacher timetabling
problem are of better quality, since, uses some function of evaluation.

There are two main problems in timetabling. The first one is related to the
combinatorial nature of the problems, where it is difficult to find an optimal
solution because it is impossible to enumerate all nodes in such a large search
space. The second one is related to the dynamic nature of the problems where
variables and constraints are changing in accordance with the development of an
organization (S.B. Deris et al., 1997). Therefore, a timetabling system must be
flexible, adaptable and portable, otherwise the users will not use the system
optimally or even as decision aids such as for storing, retrieving, and printing
timetables, when the timetable planning decisions are made manually. In
addition, most of the universities adopting a semester system give freedom to
students to choose subjects provided that all pre-requisites are satisfied. This
situation further complicates the construction of a timetable.
Various techniques have been proposed to solve timetabling problems. These
techniques are neural networks (Gianoglio P, 1990), heuristics (Wright M, 1996),
graph coloring, integer programming, Genetic Algorithms (Burke E. et al., 1994;
Paechter B. et al., 1994), knowledge-based, and constraint logic programming
(Lajos, 1995). The models formulated by some of these techniques cannot be
easily reformulated or customized to support changes, hence the selection of the
genetic algorithm for the implementation of this project.
2.2 TIMETABLING AS A NP-COMPLETE PROBLEM

In computational complexity theory, the complexity class NP-complete
(abbreviated NP-C or NPC, NP standing for Nondeterministic Polynomial time)
is a class of problems having two properties:
 Any given solution to the problem can be verified quickly (in polynomial
time); the set of problems with this property is called NP.
 If the problem can be solved quickly (in polynomial time), then so can
every problem in NP.
Although any given solution to the timetabling problem can be verified quickly,
there is no known efficient way to locate a solution in the first place; indeed, the
most notable characteristic of NP-complete problems is that no fast solution to
them is known. That is, the time required to solve the problem using any currently
known algorithm increases very quickly as the size of the problem grows (Ossam
Chohan; 2009).
When solving the timetabling problem, we are usually looking for some solution,
which will be the best among others. The space of all feasible solutions (series of
desired solutions with some more desirable than others) is called search space
(also state space). Each point in the search space represents one feasible solution
which can be "marked" by its value or fitness for the problem. The solution is
usually one point in the search space (Ossam Chohan; 2009).
As a result of comparative fact finding and exhaustive study of existing systems,
Genetic Algorithms have been the most prominently used in generating near-

optimal solutions to timetabling problems, hence its usage in the implementation
of this project.
2.3 A BRIEF HISTORY OF GENETIC ALGORITHMS
The earliest instances of what might today be called genetic algorithms appeared
in the late 1950s and early 1960s, programmed on computers by evolutionary
biologists who were explicitly seeking to model aspects of natural evolution. It
did not occur to any of them that this strategy might be more generally applicable
to artificial problems, but that recognition was not long in coming: "Evolutionary
computation was definitely in the air in the formative days of the electronic
computer" (Mitchell Melanie, 1996). By 1962, researchers such as G.E.P. Box,
G.J. Friedman, W.W. Bledsoe and H.J. Bremermann had all independently
developed evolution-inspired algorithms for function optimization and machine
learning, but their work attracted little follow-up. A more successful development
in this area came in 1965, when Ingo Rechenberg, then of the Technical
University of Berlin, introduced a technique he called evolution strategy, though
it was more similar to hill-climbers than to genetic algorithms. In this technique,
there was no population or crossover; one parent was mutated to produce one
offspring, and the better of the two was kept and became the parent for the next
round of mutation (Haupt et. al., 1998). Later versions introduced the idea of a
population. Evolution strategies are still employed today by engineers and
scientists, especially in Germany.

The next important development in the field came in 1966, when L.J. Fogel, A.J.
Owens and M.J. Walsh introduced in America a technique they called
evolutionary programming. In this method, candidate solutions to problems were
represented as simple finite-state machines; like Rechenberg's evolution strategy,
their algorithm worked by randomly mutating one of these simulated machines
and keeping the better of the two (Mitchell Melanie, 1996; Goldberg David,
1989). Also like evolution strategies, a broader formulation of the evolutionary
programming technique is still an area of ongoing research today. However, what
was still lacking in both these methodologies was recognition of the importance
of crossover.
As early as 1962, John Holland's work on adaptive systems laid the foundation
for later developments; most notably, Holland was also the first to explicitly
propose crossover and other recombination operators. However, the seminal work
in the field of genetic algorithms came in 1975, with the publication of the book
Adaptation in Natural and Artificial Systems. Building on earlier research and
papers both by Holland himself and by colleagues at the University of Michigan,
this book was the first to systematically and rigorously present the concept of
adaptive digital systems using mutation, selection and crossover, simulating
processes of biological evolution, as a problem-solving strategy. The book also
attempted to put genetic algorithms on a firm theoretical footing by introducing
the notion of schemata (Mitchell Melanie, 1996; Haupt et. al., 1998). That same
year, Kenneth De Jong's important dissertation established the potential of GAs

by showing that they could perform well on a wide variety of test functions,
including noisy, discontinuous, and multimodal search landscapes (Goldberg
David, 1989).
These foundational works established more widespread interest in evolutionary
computation. By the early to mid-1980s, genetic algorithms were being applied
to a broad range of subjects, from abstract mathematical problems like bin-
packing and graph coloring to tangible engineering issues such as pipeline flow
control, pattern recognition and classification, and structural optimization
(Goldberg David, 1989).
At first, these applications were mainly theoretical. However, as research
continued to proliferate, genetic algorithms migrated into the commercial sector,
their rise fueled by the exponential growth of computing power and the
development of the Internet. Today, evolutionary computation is a thriving field,
and genetic algorithms are "solving problems of everyday interest" (Haupt et. al.,
1998) in areas of study as diverse as stock market prediction and portfolio
planning, aerospace engineering, microchip design, biochemistry and molecular
biology, and scheduling at airports and assembly lines. The power of evolution
has touched virtually any field one cares to name, shaping the world around us
invisibly in countless ways, and new uses continue to be discovered as research
is ongoing. And at the heart of it all lies nothing more than Charles Darwin's
simple, powerful insight: that the random chance of variation, coupled with the

law of selection, is a problem-solving technique of immense power and nearly
unlimited application.
Genetic algorithms (GAs) are numerical optimization algorithms that are as a
result of both natural selection and natural genetics. The method which is general
in nature is capable of being applied to a wider range of problems unlike most
procedural approaches. Genetic algorithms help to solve practical problems on a
daily basis. The algorithms are simple to understand and the required computer
code easy to write. The Genetic Algorithm (GA) technique has never attracted
much attention like the artificial neural networks, hill climbing, simulate
annealing amongst many others although it has a growing number of disciples.
The reason for this is certainly not because of any inherent limits it has or its lack
of powerful metaphors. The phenomenon that evolution is the concept resulting
in the bio-diversity we see around us today is a powerful and inspiring paradigm
for solving any complex problem. The use of GAs have been evident from the
very beginning characterized by examples of computer scientists having visions
of systems that mimics and duplicate one or more of the attributes of life. The
idea of using a population of solutions to solve practical engineering optimization
problems was considered several times during the 1950's and 1960's. However,
the concept of GAs were essentially invented by one man—John Holland—in the
1960's. His reasons for developing such algorithms were to solve problems of
generalized concerns. He itemized this concept in his book in 1975, Adaptation
in Natural and Artificial Systems (recently re-issued with additions) which is

particularly worth reading for its visionary approach. Its application has proven
it to be more than just a robust method for estimating a series of unknown
parameters within a model of a physical system (David, 1999).
However its robustness cuts across many different practical optimization
problems especially those that concern us most like the timetable problem in the
context of this project.
2.4 BASIS FOR A GENETIC ALGORITHM
1. A number, or population, of guesses of the solution to the problem.
2. A way of determining the states of generated solutions i.e. calculating how
well or bad the individual solutions within the population are.
3. A method for mixing fragments of the better solutions to form new, on
average even better solutions.
4. A mutation operator to avoid permanent loss of diversity within the
solutions.
Concisely stated, a genetic algorithm is a programming technique that mimics
biological evolution as a problem-solving strategy. Given a specific problem to
solve, the input to the GA is a set of potential solutions to that problem, encoded
in some fashion, and a metric called a fitness function that allows each candidate
to be quantitatively evaluated. These candidates may be solutions already known
to work, with the aim of the GA being to improve them, but more often they are
generated at random.

The GA then evaluates each candidate according to the fitness function. In a pool
of randomly generated candidates, of course, most will not work at all, and these
will be deleted. However, purely by chance, a few may hold promise - they may
show activity, even if only weak and imperfect activity, toward solving the
problem.
These promising candidates are kept and allowed to reproduce. Multiple copies
are made of them, but the copies are not perfect; random changes are introduced
during the copying process. These digital offspring then go on to the next
generation, forming a new pool of candidate solutions, and are subjected to a
second round of fitness evaluation. Those candidate solutions which were
worsened, or made no better, by the changes to their code are again deleted; but
again, purely by chance, the random variations introduced into the population
may have improved some individuals, making them into better, more complete
or more efficient solutions to the problem at hand. Again these winning
individuals are selected and copied over into the next generation with random
changes, and the process repeats. The expectation is that the average fitness of
the population will increase each round, and so by repeating this process for
hundreds or thousands of rounds, very good solutions to the problem can be
discovered.
As astonishing and counterintuitive as it may seem to some, genetic algorithms
have proven to be an enormously powerful and successful problem-solving
strategy, dramatically demonstrating the power of evolutionary principles.

Genetic algorithms have been used in a wide variety of fields to evolve solutions
to problems as difficult as or more difficult than those faced by human designers.
Moreover, the solutions they come up with are often more efficient, more elegant,
or more complex than anything comparable a human engineer would produce. In
some cases, genetic algorithms have come up with solutions that baffle the
programmers who wrote the algorithms in the first place (Adam, 2004).
2.5 METHODS OF REPRESENTATION
 Before a genetic algorithm can be put to work on any problem, a method
is needed to encode potential solutions to that problem in a form that a
computer can process. One common approach is to encode solutions as
binary strings: sequences of 1's and 0's, where the digit at each position
represents the value of some aspect of the solution (Fleming et. al., 2002).
 Another, similar approach is to encode solutions as arrays of integers or
decimal numbers, with each position again representing some particular
aspect of the solution. This approach allows for greater precision and
complexity than the comparatively restricted method of using binary
numbers only and often "is intuitively closer to the problem space"
(Fleming et. al., 2002).
 A third approach is to represent individuals in a GA as strings of letters,
where each letter again stands for a specific aspect of the solution. One
example of this technique is Hiroaki Kitano's "grammatical encoding"
approach, where a GA was put to the task of evolving a simple set of rules

called a context-free grammar that was in turn used to generate neural
networks for a variety of problems (Mitchell, 1996).
The advantage of the three methods above is that they make it easy to
define operators that cause the random changes in the selected candidates:
flip a 0 to a 1 or vice versa, add or subtract from the value of a number by
a randomly chosen amount, or change one letter to another.
 Another strategy, developed principally by John Koza of Stanford
University and called genetic programming, represents programs as
branching data structures called trees (Koza et. al., 2003). In this approach,
random changes can be brought about by changing the operator or altering
the value at a given node in the tree, or replacing one sub-tree with another.
Figure 2.5: Three simple program trees of the kind normally used in genetic
programming. The mathematical expression that each one represents is given
underneath it (Adapted from Adam Marczyk 2004).
It is important to note that evolutionary algorithms do not necessarily represent
candidate solutions as data strings of fixed length. Though some represent them
this way, but others do not; e.g. Kitano's grammatical encoding discussed above

can be efficiently scaled to create large and complex neural networks, and Koza's
genetic programming trees can grow arbitrarily large as necessary to solve
whatever problem they are applied to.
2.6 METHODS OF SELECTION
There are many different techniques which a genetic algorithm can use to select
the individuals to be copied over into the next generation, but listed below are
some of the most common methods. Some of these methods are mutually
exclusive, but others can be and often are used in combination.
 Elitist selection: The fittest members of each generation are guaranteed to
be selected. (Most GAs doesn’t use pure elitism, but instead use a modified
form where the single best or a few of the best individuals from each
generation are copied into the next generation just in case nothing better
turns up.)
 Fitness-proportionate selection: More fit individuals are more likely, but
not certain, to be selected.
 Roulette-wheel selection: A form of fitness-proportionate selection in
which the chance of an individual's being selected is proportional to the
amount by which its fitness is greater or less than its competitors' fitness.
(Conceptually, this can be represented as a game of roulette - each
individual gets a slice of the wheel, but more fit ones get larger slices than
less fit ones. The wheel is then spun, and whichever individual "owns" the
section on which it lands each time is chosen).

 Scaling selection: As the average fitness of the population increases, the
strength of the selective pressure also increases and the fitness function
becomes more discriminating. This method can be helpful in making the
best selection later on when all individuals have relatively high fitness and
only small differences in fitness distinguish one from another.
 Tournament selection: Subgroups of individuals are chosen from the
larger population, and members of each subgroup compete against each
other. Only one individual from each subgroup is chosen to reproduce.
 Rank selection: Each individual in the population is assigned a numerical
rank based on fitness, and selection is based on this ranking rather than
absolute difference in fitness. The advantage of this method is that it can
prevent very fit individuals from gaining dominance early at the expense
of less fit ones, which would reduce the population's genetic diversity and
might hinder attempts to find an acceptable solution.
 Generational selection: The offspring of the individuals selected from
each generation become the entire next generation. No individuals are
retained between generations.
 Steady-state selection: The offspring of the individuals selected from each
generation go back into the pre-existing gene pool, replacing some of the
less fit members of the previous generation. Some individuals are retained
between generations.

 Hierarchical selection: Individuals go through multiple rounds of
selection each generation. Lower-level evaluations are faster and less
discriminating, while those that survive to higher levels are evaluated more
rigorously. The advantage of this method is that it reduces overall
computation time by using faster, less selective evaluation to weed out the
majority of individuals that show little or no promise, and only subjecting
those who survive this initial test to more rigorous and more
computationally expensive fitness evaluation.
2.7 METHODS OF CHANGE
 Once selection has chosen fit individuals, they must be randomly altered
in hopes of improving their fitness for the next generation. There are two
basic strategies to accomplish this. The first and simplest is called
mutation. Just as mutation in living things changes one gene to another,
so mutation in a genetic algorithm causes small alterations at single points
in an individual's code. Refer to Figure.
Figure 2.7a: Diagram showing the effect of mutation on an individual in a
population of 8-bit strings where mutation occurs at position 4, changing the 0 at
that position in its genome to a 1 (Adapted from Adam Marczyk 2004).

 The second method is called crossover, and entails choosing two
individuals to swap segments of their code, producing artificial "offspring"
that are combinations of their parents. This process is intended to simulate
the analogous process of recombination that occurs to chromosomes during
sexual reproduction (Adam, 2004). Common forms of crossover include
single-point crossover, in which a point of exchange is set at a random
location in the two individuals' genomes, and one individual contributes all
its code from before that point and the other contributes all its code from
after that point to produce an offspring, and uniform crossover, in which
the value at any given location in the offspring's genome is either the value
of one parent's genome at that location or the value of the other parent's
genome at that location, chosen with 50/50 probability. Refer to Figure
2.7b.
Figure 2.7b: Diagram showing the effect of mutation on individuals in a
population of 8-bit strings showing two individuals undergoing single-point
crossover; the point of exchange is set between the fifth and sixth positions in the
genome, producing a new individual that is a hybrid of its progenitors (Adapted
from Adam Marczyk 2004).

2.8 STRENGTHS OF GENETIC ALGORITHMS
 The first and most important point is that genetic algorithms are
intrinsically parallel. Most other algorithms are serial and can only explore
the solution space to a problem in one direction at a time, and if the solution
they discover turns out to be suboptimal, there is nothing to do but abandon
all work previously completed and start over. However, since GAs has
multiple offspring, they can explore the solution space in multiple
directions at once. If one path turns out to be a dead end, they can easily
eliminate it and continue work on more promising avenues, giving them a
greater chance each run of finding the optimal solution (Adam, 2004; John,
1992).
 However, the advantage of parallelism goes beyond this. Consider the
following: All the 8-digit binary strings (strings of 0's and 1's) form a
search space, which can be represented as ******** (where the * stands
for "either 0 or 1"). The string 01101010 is one member of this space.
However, it is also a member of the space 0*******, the space 01******,
the space 0******0, the space 0*1*1*1*, the space 01*01**0, and so on.
By evaluating the fitness of this one particular string, a genetic algorithm
would be sampling each of these many spaces to which it belongs. Over
many such evaluations, it would build up an increasingly accurate value
for the average fitness of each of these spaces, each of which has many
members. Therefore, a GA that explicitly evaluates a small number of

individuals is implicitly evaluating a much larger group of individuals -
just as a pollster who asks questions of a certain member of an ethnic,
religious or social group hopes to learn something about the opinions of all
members of that group, and therefore can reliably predict national opinion
while sampling only a small percentage of the population. In the same way,
the GA can "home in" on the space with the highest-fitness individuals and
find the overall best one from that group. In the context of evolutionary
algorithms, this is known as the Schema Theorem, and is the "central
advantage" of a GA over other problem-solving methods (John, 1992;
Mitchell, 1996; Goldberg, 1989).
 Due to the parallelism that allows them to implicitly evaluate many
schemas at once, genetic algorithms are particularly well-suited to solving
problems where the space of all potential solutions is truly huge - too vast
to search exhaustively in any reasonable amount of time. Most problems
that fall into this category are known as "nonlinear". In a linear problem,
the fitness of each component is independent, so any improvement to any
one part will result in an improvement of the system as a whole. Needless
to say, few real-world problems are like this. Nonlinearity is the norm,
where changing one component may have ripple effects on the entire
system, and where multiple changes that individually are detrimental may
lead to much greater improvements in fitness when combined. Nonlinearity
results in a combinatorial explosion: the space of 1,000-digit binary strings

can be exhaustively searched by evaluating only 2,000 possibilities if the
problem is linear, whereas if it is nonlinear, an exhaustive search requires
evaluating 21000 possibilities - a number that would take over 300 digits
to write out in full (Adam, 2004).
 Fortunately, the implicit parallelism of a GA allows it to surmount even
this enormous number of possibilities, successfully finding optimal or very
good results in a short period of time after directly sampling only small
regions of the vast fitness landscape (Forrest, 1993). For example, a genetic
algorithm developed jointly by engineers from General Electric and
Rensselaer Polytechnic Institute produced a high-performance jet engine
turbine design that was three times better than a human-designed
configuration and 50% better than a configuration designed by an expert
system by successfully navigating a solution space containing more than
10387 possibilities. Conventional methods for designing such turbines are
a central part of engineering projects that can take up to five years and cost
over $2 billion; the genetic algorithm discovered this solution after two
days on a typical engineering desktop workstation (John, 1992).
 Another notable strength of genetic algorithms is that they perform well in
problems for which the fitness landscape is complex - ones where the
fitness function is discontinuous, noisy, changes over time, or has many
local optima. Most practical problems have a vast solution space,
impossible to search exhaustively; the challenge then becomes how to

avoid the local optima - solutions that are better than all the others that are
similar to them, but that are not as good as different ones elsewhere in the
solution space. Many search algorithms can become trapped by local
optima: if they reach the top of a hill on the fitness landscape, they will
discover that no better solutions exist nearby and conclude that they have
reached the best one, even though higher peaks exist elsewhere on the map
(Adam, 2004).
 Evolutionary algorithms, on the other hand, have proven to be effective at
escaping local optima and discovering the global optimum in even a very
rugged and complex fitness landscape. (It should be noted that, in reality,
there is usually no way to tell whether a given solution to a problem is the
one global optimum or just a very high local optimum. However, even if a
GA does not always deliver a provably perfect solution to a problem, it can
almost always deliver at least a very good solution.) All four of a GA's
major components - parallelism, selection, mutation, and crossover - work
together to accomplish this. In the beginning, the GA generates a diverse
initial population, casting a "net" over the fitness landscape. (Koza et. al.,
2003) compares this to an army of parachutists dropping onto the landscape
of a problem's search space, with each one being given orders to find the
highest peak.) Small mutations enable each individual to explore its
immediate neighborhood, while selection focuses progress, guiding the

algorithm's offspring uphill to more promising parts of the solution space
(John, 1992).
 However, crossover is the key element that distinguishes genetic
algorithms from other methods such as hill-climbers and simulated
annealing. Without crossover, each individual solution is on its own,
exploring the search space in its immediate vicinity without reference to
what other individuals may have discovered. However, with crossover in
place, there is a transfer of information between successful candidates -
individuals can benefit from what others have learned, and schemata can
be mixed and combined, with the potential to produce an offspring that has
the strengths of both its parents and the weaknesses of neither. This point
is illustrated in Koza et.al. (1999), where the authors discuss a problem of
synthesizing a low pass filter using genetic programming. In one
generation, two parent circuits were selected to undergo crossover; one
parent had good topology (components such as inductors and capacitors in
the right places) but bad sizing (values of inductance and capacitance for
its components that were far too low). The other parent had bad topology,
but good sizing. The result of mating the two through crossover was an
offspring with the good topology of one parent and the good sizing of the
other, resulting in a substantial improvement in fitness over both its
parents.

 The problem of finding the global optimum in a space with many local
optima is also known as the dilemma of exploration vs. exploitation, "a
classic problem for all systems that can adapt and learn" (John, 1992). Once
an algorithm (or a human designer) has found a problem-solving strategy
that seems to work satisfactorily, should it concentrate on making the best
use of that strategy, or should it search for others? Abandoning a proven
strategy to look for new ones is almost guaranteed to involve losses and
degradation of performance, at least in the short term. But if one sticks with
a particular strategy to the exclusion of all others, one runs the risk of not
discovering better strategies that exist but have not yet been found. Again,
genetic algorithms have shown themselves to be very good at striking this
balance and discovering good solutions with a reasonable amount of time
and computational effort (Adam, 2004).
 Another area in which genetic algorithms excel is their ability to
manipulate many parameters simultaneously (Forrest, 1993). Many real-
world problems cannot be stated in terms of a single value to be minimized
or maximized, but must be expressed in terms of multiple objectives,
usually with tradeoffs involved: one can only be improved at the expense
of another. GAs are very good at solving such problems: in particular, their
use of parallelism enables them to produce multiple equally good solutions
to the same problem, possibly with one candidate solution optimizing one
parameter and another candidate optimizing a different one (Haupt et.al.,

1998), and a human overseer can then select one of these candidates to use.
If a particular solution to a multi-objective problem optimizes one
parameter to a degree such that that parameter cannot be further improved
without causing a corresponding decrease in the quality of some other
parameter, that solution is called Pareto optimal or non-dominated (Coello,
2000).
 Finally, one of the qualities of genetic algorithms which might at first
appear to be a liability turns out to be one of their strengths: namely, GAs
know nothing about the problems they are deployed to solve. Instead of
using previously known domain-specific information to guide each step
and making changes with a specific eye towards improvement, as human
designers do, they are "blind watchmakers" (Dawkins, 1996); they make
random changes to their candidate solutions and then use the fitness
function to determine whether those changes produce an improvement.
 The virtue of this technique is that it allows genetic algorithms to start out
with an open mind, so to speak. Since its decisions are based on
randomness, all possible search pathways are theoretically open to a GA;
by contrast, any problem-solving strategy that relies on prior knowledge
must inevitably begin by ruling out many pathways a priori, therefore
missing any novel solutions that may exist there (Koza et. al., 1999).
Lacking preconceptions based on established beliefs of "how things should
be done" or what "couldn't possibly work", GAs do not have this problem.

Similarly, any technique that relies on prior knowledge will break down
when such knowledge is not available, but again, GAs is not adversely
affected by ignorance (Goldberg, 1989). Through their components of
parallelism, crossover and mutation, they can range widely over the fitness
landscape, exploring regions which intelligently produced algorithms
might have overlooked, and potentially uncovering solutions of startling
and unexpected creativity that might never have occurred to human
designers. One vivid illustration of this is the rediscovery, by genetic
programming, of the concept of negative feedback - a principle crucial to
many important electronic components today, but one that, when it was
first discovered, was denied a patent for nine years because the concept
was so contrary to established beliefs (Koza et. al., 2003). Evolutionary
algorithms, of course, are neither aware nor concerned whether a solution
runs counter to established beliefs - only whether it works.
2.9 LIMITATIONS OF GENETIC ALGORITHMS
Although genetic algorithms have proven to be an efficient and powerful
problem-solving strategy, they are not a panacea. GAs does have certain
limitations which are outlined below:
 The first, and most important, consideration in creating a genetic algorithm
is defining a representation for the problem. The language used to specify
candidate solutions must be robust; i.e., it must be able to tolerate random
changes such that fatal errors or nonsense do not consistently result.

There are two main ways of achieving this. The first, which is used by most
genetic algorithms, is to define individuals as lists of numbers - binary-
valued, integer-valued, or real-valued - where each number represents
some aspect of a candidate solution. If the individuals are binary strings, 0
or 1 could stand for the absence or presence of a given feature. If they are
lists of numbers, these numbers could represent many different things: the
weights of the links in a neural network, the order of the cities visited in a
given tour, the spatial placement of electronic components, the values fed
into a controller, the torsion angles of peptide bonds in a protein, and so
on. Mutation then entails changing these numbers, flipping bits or adding
or subtracting random values. In this case, the actual program code does
not change; the code is what manages the simulation and keeps track of the
individuals, evaluating their fitness and perhaps ensuring that only values
realistic and possible for the given problem result.
In another method, genetic programming, the actual program code does
change. As discussed in the section Methods of representation, GP
represents individuals as executable trees of code that can be mutated by
changing or swapping sub-trees. Both of these methods produce
representations that are robust against mutation and can represent many
different kinds of problems, and both have had considerable success in
various examples on which they have been applied.

This issue of representing candidate solutions in a robust way does not arise
in nature, because the method of representation used by evolution, namely
the genetic code, is inherently robust: with only a very few exceptions, such
as a string of stop codons, there is no such thing as a sequence of DNA
bases that cannot be translated into a protein. Therefore, virtually any
change to an individual's genes will still produce an intelligible result, and
so mutations in evolution have a higher chance of producing an
improvement. This is in contrast to human-created languages such as
English, where the number of meaningful words is small compared to the
total number of ways one can combine letters of the alphabet, and therefore
random changes to an English sentence are likely to produce nonsense
(Adam, 2004).
 The problem of how to write the fitness function must be carefully
considered so that higher fitness is attainable and actually does equate to a
better solution for the given problem. If the fitness function is chosen
poorly or defined imprecisely, the genetic algorithm may be unable to find
a solution to the problem, or may end up solving the wrong problem. (This
latter situation is sometimes described as the tendency of a GA to "cheat",
although in reality all that is happening is that the GA is doing what it was
told to do, not what its creators intended it to do.) This is not a problem in
nature, however. In the laboratory of biological evolution there is only one
fitness function, which is the same for all living things - the drive to survive

and reproduce, no matter what adaptations make this possible. Those
organisms which reproduce more abundantly compared to their
competitors are fitter; those which fail to reproduce are unfit (Adam, 2004).
 In addition to making a good choice of fitness function, the other
parameters of a GA - the size of the population, the rate of mutation and
crossover, the type and strength of selection - must be also chosen with
care. If the population size is too small, the genetic algorithm may not
explore enough of the solution space to consistently find good solutions. If
the rate of genetic change is too high or the selection scheme is chosen
poorly, beneficial schema may be disrupted and the population may enter
error catastrophe, changing too fast for selection to ever bring about
convergence (Adam, 2004).
Living things do face similar difficulties, and evolution has dealt with
them. It is true that if a population size falls too low, mutation rates are too
high, or the selection pressure is too strong (such a situation might be
caused by drastic environmental change), then the species may go extinct.
The solution has been "the evolution of evolvability" - adaptations that alter
a species' ability to adapt. For example, most living things have evolved
elaborate molecular machinery that checks for and corrects errors during
the process of DNA replication, keeping their mutation rate down to
acceptably low levels; conversely, in times of severe environmental stress,
some bacterial species enter a state of hyper mutation where the rate of

DNA replication errors rises sharply, increasing the chance that a
compensating mutation will be discovered. Of course, not all catastrophes
can be evaded, but the enormous diversity and highly complex adaptations
of living things today show that, in general, evolution is a successful
strategy. Likewise, the diverse applications of and impressive results
produced by genetic algorithms show them to be a powerful and
worthwhile field of study (John, 1975).
 One type of problem that genetic algorithms have difficulty dealing with
are problems with "deceptive" fitness functions (Mitchell, 1996), those
where the locations of improved points give misleading information about
where the global optimum is likely to be found. For example, imagine a
problem where the search space consisted of all eight-character binary
strings, and the fitness of an individual was directly proportional to the
number of 1s in it - i.e., 00000001 would be less fit than 00000011, which
would be less fit than 00000111, and so on - with two exceptions: the string
11111111 turned out to have very low fitness, and the string 00000000
turned out to have very high fitness. In such a problem, a GA (as well as
most other algorithms) would be no more likely to find the global optimum
than random search.
The resolution to this problem is the same for both genetic algorithms and
biological evolution: evolution is not a process that has to find the single
global optimum every time. It can do almost as well by reaching the top of

a high local optimum, and for most situations, this will suffice, even if the
global optimum cannot easily be reached from that point. Evolution is very
much a "satisfier" - an algorithm that delivers a "good enough" solution,
though not necessarily the best possible solution, given a reasonable
amount of time and effort invested in the search. The Evidence for Jury-
Rigged Design in Nature FAQ gives examples of this very outcome
appearing in nature. (It is also worth noting that few, if any, real-world
problems are as fully deceptive as the somewhat contrived example given
above. Usually, the location of local improvements gives at least some
information about the location of the global optimum.)
 One well-known problem that can occur with a GA is known as premature
convergence. If an individual that is more fit than most of its competitors
emerges early on in the course of the run, it may reproduce so abundantly
that it drives down the population's diversity too soon, leading the
algorithm to converge on the local optimum that that individual represents
rather than searching the fitness landscape thoroughly enough to find the
global optimum (Forrest, 1993; Mitchell, 1996). This is an especially
common problem in small populations, where even chance variations in
reproduction rate may cause one genotype to become dominant over
others.
The most common methods implemented by GA researchers to deal with
this problem all involve controlling the strength of selection, so as not to

give excessively fit individuals too great of an advantage. Rank, scaling
and tournament selection, discussed earlier, are three major means for
accomplishing this; some methods of scaling selection include sigma
scaling, in which reproduction is based on a statistical comparison to the
population's average fitness, and Boltzmann selection, in which the
strength of selection increases over the course of a run in a manner similar
to the "temperature" variable in simulated annealing (Mitchell, 1996).
Premature convergence does occur in nature (where it is called genetic drift
by biologists). This should not be surprising; as discussed above, evolution
as a problem-solving strategy is under no obligation to find the single best
solution, merely one that is good enough. However, premature
convergence in nature is less common since most beneficial mutations in
living things produce only small, incremental fitness improvements;
mutations that produce such a large fitness gain as to give their possessors
dramatic reproductive advantage are rare.
 Finally, several researchers (John, 1992; Forrest, 1993; Haupt et. al., 1998)
advise against using genetic algorithms on analytically solvable problems.
It is not that genetic algorithms cannot find good solutions to such
problems; it is merely that traditional analytic methods take much less time
and computational effort than GAs and, unlike GAs, are usually
mathematically guaranteed to deliver the one exact solution. Of course,

since there is no such thing as a mathematically perfect solution to any
problem of biological adaptation, this issue does not arise in nature.
2.10 APPLICATION OF GENETIC ALGORITHMS IN THIS
RESEARCH
Having considered the basis for a genetic algorithm, the outline below highlights
the applications of the proposed system in generating timetables (with
Department of Computer Science, Akanu Ibiam Federal Polytechnic, Unwana as
case study).
The timetabling problem is a combinatorial optimization problem (COP) and in
order to find a very comprehensive mathematical framework to describe it (and
also tackle its NP-hardness), hence the introduction of a highly abstract concept
of heuristics (genetic algorithms). The basic property of the timetable problem is
the attempt of the genetic algorithm to optimize a function over a discrete
structure with many independent variables. The relation between the choices
made in the discrete domain and the effects on the objective function value are
usually complex and frequently not easy to trace. The unifying framework for
COP’s is the Constraint Satisfaction Problem (CSP) in conjunction with the
optimization of an objective function (Kostuch, 2003). It is worthy of note that
even though the timetabling problem is treated as an optimization problem, there
is actually no fixed objective function, the function that exists is used as an
arbitrary measure to check for optimized solutions and degree of constraints
satisfaction (Abramson, 1992). Once the objectives and constraints are specified,

genetic algorithms offer the ultimate scenarios of good timetable solutions
through evolution processes even though the complexity of assignment is totally
dependent on the number of instances and number of constraints.
Hence the algorithm considered for use in the proposed system is a scaled down
version of the Hybrid Genetic algorithm for the construction of examination
timetables developed for the University of Nottingham. The concept though
developed for examination timetabling, can be adapted to fit the construction of
course timetables. The genetic algorithm employed combines two heuristic
algorithms, the first finding a non-conflicting set of exams and the second
assigning the selected exam to rooms. The process is repeated until all exams
have been scheduled without conflicts (Rupert, 1995).

Figure 2.10: Diagram depicting the Hybrid Genetic Algorithm used in University
of Nottingham (Adapted from Rupert Weare et. al. 1995).
Like every other genetic algorithms, this algorithm can quickly produce large
populations of random feasible exam timetables. Uniquely, the process takes
each member of the course population and assigns it to the first period in which
the exam may be placed without conflict. The mutation and crossover
procedures are then applied to the population so that constraints associated
with each course in the assignment are satisfied. The timetables generated by
the algorithm with a starting population size of 200 had an average fitness of
0.986 (Rupert Weare et. al., 1995).

CHAPTER THREE
SYSTEM INVESTIGATION AND ANALYSIS
A system can be defined as a group of components consisting of sub system or
procedures that works in a coordinated fashion to achieve said objective.
This chapter reviews how the existing system works as well as to produce a better
alternative for its improvement.
The relationship amongst entities and information flows within the organization
is very important; in a nutshell, system investigation and analysis is the study of
an existing system with the view of improving on it or developing an entirely new
system to replace the existing one.
The major task here is to design a new system with a mobile frontend for students.

FUNCTIONS OF THE PERSONNEL
1. H.O.D: He is the head of the department. He plans and assigns work
schedule for each staff and students, and he oversees their duties.
2. SECRETARY/TYPIST: She is a subordinate staff and responsible to the
head of department. She is responsible for typing official document
needed for the academic duties of the department, receiving of incoming
mails, and issuing of course forms to students.
3. LECTURERS: A lecturer lectures students professionally.
4. TECHNOLOGIST: The technologist uses scientific knowledge to solve
practical problems.
5. ADMINISTRATION OFFICICER: He is responsible for the
administrative management of the office.
6. EXECUTIVE OFFICIER: He is also responsible for the administration of
business, he has the function of carrying out plans orders etc.
7. LAB ASSISTANT: The lab assistant is in charge of taking care of the
laboratory.
8. CLERICAL OFFICERS/MESSENGER: They are in charge of clerical
duties and running errands for the department.
3.2FACTS FINDING
Fact finding is an approach taken to acquire data about a specific or subject with
the aim of analyzing and synthesizing the analyzed data to come up with a better

system. The researcher used different techniques in data collection in this
research and they include: interview, examination of existing documents, internet
browsing and use of questionnaires.
A set of well-articulated questions were used to interview a number of persons.
During the investigation, some documents used for drafting the lecture timetable
were examined. Some of them include:
i. List of examinable and non-examinable courses offered by the
department.
ii. List of existing lecture halls and their carrying capacity.
iii. List of periods or intervals.
The internet is the largest pool of information where virtually everything can be
found. The researcher in addition to examining existing documents equally used
the internet. Some relevant and related documents were downloaded.
The researcher used the questionnaire to take conclusion on how the user
interface will be. It was developed using Google Forms.
3.3ANALYSIS
In this section, thorough studying and analysis of the gathered data and fact were
done on the existing system.
In Akanu Ibiam Federal Polytechnic Unwana, timetable scheduling is done by:

 The submission of examinable and non-examinable courses by the
timetable officer of each department.
 The submitted documents are taken by the chairman space and timetable
committee.
 Previous school scheduled timetable and blank templates are passed round
from one department to another for the timetable officers to allocate course
to halls.
 After the exercise, tentative timetables are released for staff and students
to make inputs.
 At the end of receiving inputs from students and lectures alike, the final
timetable is presented to the school and it will become a working document
for that semester.
3.4PROBLEMS OF THE CURRENT SYSTEM
The traditional manual generations of timetables encounters a lot of problems
which may include the following:
 Repeated time allocations may be made for a particular course thereby
leading to data redundancy.
 A lot of administrative error may occur as a result of confusing time
requirements.
 Timetable generation by center staff may have a slow turnaround.

 Final generated timetable may not be near optimal as a result of clashing
course requirements and allocations.
 It generates a lot of paperwork and is very tasking.
 It is not flexible as changes may not be easily made.
3.5PROPOSING A NEW SYSTEM
The proposed systems was developed to solve the timetabling problem being
faced by universities every academic year and reduce high cost and slow
turnaround involved in the generation of near-optimal timetables.
The system has capabilities for input of the various courses, halls of lectures,
departments, programs, buildings, lecturers and the specification of a few
constraints from which the timetable is constructed. The proposed timetabling
system for this project seeks to generate near optimal timetables using the
principles of genetic algorithm (selection and crossover).
3.6 ADVANTAGES OF THE PROPOSED SYSTEM
The timetable generation process by the education center staff is:
 Unlike the manual timetabling system, the system offers flexibility.
 It utilizes minimal processing/computing power.
 It greatly reduces the time needed to generate near-optimal timetables.
 It provides an easy means for data entry and revision through an intuitive
interface.
 It increases productivity.

 Timetables generated are between to 60% - 80% optimum.
 It almost eliminates paperwork.
 It simplifies the timetabling process.

CHAPTER FOUR
SYSTEM DESIGN
System design is the specification or construction of a technical, computer-based
solution for the business requirements identified in system analysis. It gives the
overall plan or model of a system consisting of all specification that give the
system its form and structure (i.e. the structural implementation of the system
analysis).
4.1 OBJECTIVES OF THE DESIGN
The objective of designing the new system is to provide an automated timetabling
system that will generate a feasible timetable and broadcast to listening devices
(android).
FACTORS CONSIDERED IN THE DESIGN
The following are the factors put into consideration during the design of the new
system;
To design
 A user friendly system.
 A system that is able to consider all given constraints before producing a
feasible (timetable) output.
 A time effective system.
 A system that will broadcast generated timetable to listening android

devices once it has been published.
4.2SYSTEM BLOCK DIAGRAM
Fig 4.2: System Block Diagram
COURSE
DATA
DEPT.
DATA
LECTURER
DATA
ROOM
DATA
COURSE
READ
DEPT.
READ
LECTURER
READ
ROOM
READ PREVIEW
COURSE
ADD
DEPT.
ADD
LECTURER
ADD
ROOM
ADD ADD
DATABASE
MANAGEMENT
SYSTEM
COURSE
UPDATE
DEPT.
UPDATE
LECTURER
UPDATE
ROOM
UPDATE UPDATE
COURSE
DELETE
DEPT.
DELETE
LECTURER
DELETE
ROOM
DELETE DELETE
BROADCASTGENERATE TIMETABLE

4.3OUTPUT DESIGN
The output design contains all the necessary details of the processed input by the
system which is drawn from different tables of the database. In this case, the
generated timetable is the output of the new system.
4.4INPUT DESIGN
The input design is the interaction point between the user and the system. There
are three major input forms/design
 Courses input design.
 Room input design.
 Lecturer input design.
 Building input design.
 Class input design.
4.5 PROGRAM DESIGN
This is where the programs that will run the modules identified in the control
center are specified. This will enable the researcher to capture the complete
working picture of the application, how each component is related to another, the
calling program modules and the ones called.
The timetabling system consists of various program modules. Each module of the
system is a complete module and part of the entire system.
Below are the various modules contained in the application:

 Course Module
The course module is the interface (subsystem) that manages the courses
available in the database. To create a new course, the course name (title), course
code, course description and course lecturer are required fields. The course
lecturer field is fetched from the lecturers table.
Fig 4.5a: Course Input Module
 Building Module
This subsystem manages the existing building on the database. To create a new
building record, the building name is a required field.

Fig 4.5b: Building Input Module
 Room Module
The room module/subsystem manages available rooms in the database. To create
a new record, the room name, building name and room capacity are required
fields. The building name field is fetched from the buildings table.
Fig 4.5b: Room Input Module
 Lecturer Module
This subsystem manages available lecturers in the database. To add a new
lecturer, the lecturer name and department is required field.

Fig 4.5b: Lecturer Module
 Class Module
The class subsystem manages available classes in the database. To insert new
record, the level, class size and department are required.
4.6 DATABASE
This is an organized collection and manipulation of data. The data are typically
organized to model relevant aspect of reality in a way that supports the process
requiring the information.
4.7 DATABASE SPECIFICATION
The program's database is built using MySQL.

4.8 PROGRAM FLOWCHART
This describes the diagrammatic representation of the logical paths to be
followed.

Fig 4.8: Program Flowchart
LOGIN MODULE
This is the first module that is loaded once the web timetable interface is
launched. It accepts three information (login ID, login PIN and remember me -
optional) that will be validated against the one already stored in the users database
table. If the login information is valid, a new session will be created for the user
and the dashboard page will be loaded, else an error message based on the offence
committed will be displayed and the login page will be reloaded.
DASHBOARD
This module is loaded if and only if a session was created for the timetable officer.
This is the module that housed other subsystems – Course module, Lecturer
module, Rooms module and Timetable Scheduling module.
4.9 MODELING THE SYSTEM
Modeling a system is the process of abstracting and organizing significant
features of how the system would look like. Modeling is the designing of the
software application before coding. Unified Modeling Language (UML) tools
were used in modeling the system.
UML (Unified Modeling Language)
This is the object-oriented system notation that provides a set of modeling
conventions that is used to specify or describe a software system in terms of

objects. The UML has become an object modeling standard and adds a variety of
techniques to the field of system analysis and development hence its choice for
this project.
UML offers ten different diagrams to model a system. These diagrams are listed
below:
 Use case diagram
 Class diagram
 Object diagram
 Sequence diagram
 Collaboration diagram
 State diagram
 Activity diagram
 Component diagram
 Deployment diagram
 Package Diagram
In this project, the Use case diagram, Class diagram, Sequence diagram, Activity
diagram and State diagram will be used for system modeling.

USE CASE DIAGRAM
Use case diagrams describe what a system does from an external observer's
standpoint. The emphasis of use case diagrams is on what a system does rather
than how. They are used to show the interactions between users of the system and
the system. A use case represents the several users called actors and the different
ways in which they interact with the system.
Fig 4.9a: Use Case Diagram

CLASS DIAGRAM
This is an organization of related objects. It gives an overview of a system by
showing its classes and their relationships. Class diagrams only displays what
interacts and not what happens during the interaction.
Fig 4.9b: Class Diagram

SEQUENCE DIAGRAM
This describes how objects interact with each other through messages during the
execution of a use case or any operation. They illustrate how messages are sent
and received between objects and the sequence of message transfer. It also
describes how operations are carried out according to the time of operation.
Fig 4.9c: Sequence Diagram

ACTIVITY DIAGRAM
Activity diagrams describe the sequential flow of activities of either a business
process or use case. They can also be used to model actions that will be performed
when an operation is executed as well as the result of those actions. It shows how
activities depend on one another.
Fig 4.9d: Activity Diagram

STATE DIAGRAM
This is used to model the dynamic behavior of a particular object. They illustrates
an object's lifecycle (i.e. the various states that an object can assume) and the
events that cause the object to move from one state to another.
4.10 CHOICE OF PROGRAMMING LANGUAGE
The web backend is heavily built using HTML programming language. The
HTML is a native language for preparing document for presentation on the
internet. In addition to HTML, PHP was used also. PHP is an open source server-
side HTML-embedded scripting language. PHP here serves as the server-side

processor to process constraints, generate feasible output and make available to
all listening devices.

CHAPTER FIVE
SYSTEM DOCUMENTATION AND IMPLEMENTATION
The system implementation defines the construction, installation, testing and
delivery of the proposed system. After thorough analysis and design of the
system, the system implementation incorporates all other development phases to
produce a functional system.
5.1 SYSTEM REQUIREMENTS
Below are the conditions required on a computer system for the timetable to
run;
HARDWARE REQUIREMENTS
 Processor should be Pentium V and above
 A minimum of 1Gigabyte of RAM
 Hard Disk space of 80Gigabyte and above.
For the mobile interface,
 Android device
 A minimum of 512megabyte of RAM.
SOFTWARE REQUIREMENTS
 Linux, Windows, Mac Operating Systems.
 PC Web Browser – Mozilla, Google Chrome, Internet Explorer, Safari

etc.
For the mobile interface,
 A minimum version of Android 4.2.2 (Kitkat)
5.2 HOW TO INSTALL
The software installation process requires a live hosting of the web application
on either a public or a private web server. The android application must also be
installed on an android device. Provided the web and mobile application has
been hosted and installed successfully, then it is ready for use.
5.3TRAINING OF OPERATORS
This is very important for the new system. For the new system to be successful
the steps involved in the training of timetable officers and site administrators are
as follows;
 Educating the users on how to use the internet.
 Educate the users on the objectives and benefits of the new system.
 The users will also be educated on roles management as well as their
duties in the new system.
 In other to actualize the above and enhance the user training, workshops
will be organized for operators.
 Making the system documentation available to users.
5.4 IMPLEMENTATION METHOD

This is concerned with the way the system will be implemented. The activities
involved in system implementation and programming, testing the system,
training of operators and conversion from the old system to the new system.
a. PROGRAMMING
The conversion of the design specification into computer readable language is
performed here. Subsystem modules were developed and compiled to produce
the new system. Many frameworks were employed to build the web application
backend.
 jQuery (JavaScript framework)
 Bootstrap (CSS framework)
 ionicframework (CSS framework)
 CodeIgniter (PHP framework)
For the mobile frontend, java and xml were used for the development.
b. TESTING THE SYSTEM
The new system was tested with the required constraints and the output was a
feasible timetable solution.
c. CHANGE OVER PROCEDURES
This process involves changing from the existing procedure to an online
timetable management system. The parallel conversion method was chosen.
This is to allow both the old and new system run simultaneously for some time

to ensure that the new system meets the requirements it is introduced to. Other
conversion methods include
 Direct Changeover Method
This is mainly used where there is a strong confidence in the design and
implementation of the new system. It involves complete changeover of the old
system when the new system has been implemented.
 Pilot Changeover Method
This involves pre-testing the new system in a separate or isolated testing
environment using the same form of data. This method provides an opportunity
of testing the systems operational reliability and risk of changing everything at
once.
 Phased Changeover Method
Here, conversion is carried out gradually until the entire conversion is
completed. This is used when the old and new systems look alike.
5.5 REVIEW AND MAINTENANCE OF THE SYSTEM
It is necessary to check the new system periodically to present unforeseen
problems that might arise in the cause of using the system and to give room for
system upgrade when necessary. File maintenance operations includes:
– Record deletion operation.

– Record insertion operational.
– Record rearrangement and amendment.
Due to numerous advantages offered to this new system through its index-
sequential method of file accessing, file maintenance operations are easily done.
FILE SECURITY
The researcher used the following items to ensure adequate security of system.
 Encryption of files using CodeIgniter’s encryption key.
 Backup copies of database.
 Effective use of passwords for user authentication.
 Restricting access for non-administrative positions.

CHAPTER SIX
SUMMARY, CONCLUSION AND RECOMMENDATION
6.1SUMMARY
This study was carried out as is to reduce the intense manual effort being put into
creating and developing school lecture timetables. The timetable automation
system currently is a conceptual work in progress but has the capability to
generate near-optimal timetables based on three unit courses with minimized
course constraints.
6.2PROBLEMS ENCOUNTERED
Timing constraints was a major issue in the development of this system due to
the robustness of the system. Given the time allotment for this project, the system
developed could not meet up to the intended robustness.
6.3CONCLUSION
Timetabling problem being the hard combinatorial problem that is would take
more than just the application of only one principle. The timetable problem may
only be solved when the constraints and allocations are clearly defined and
simplified thoroughly and more than one principle is applied to it (i.e. A hybrid
solution – a combination of different solution techniques).

6.4CONTRIBUTION TO KNOWLEDGE
Timetable problem is NP-Complete. Timetable management has been made
easier with this solution. It includes a web app to generate timetable and push to
android devices.
6.5RECOMMENDATIONS
Having understudied the challenges that are contained in the manual timetable
system, the following are recommended
1. The timetable system developed should be broadened to accommodate the
timetable management of the entire department (academic) in higher
institution and made open to all students.
2. Further works on developing a timetable system should be based this
research work so as to utilize the incremental model of software
development.
3. A collaborative model of timetabling system which utilizes mobile
operating systems should be built.

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APPENDIX I
PROGRAM SOURCE CODE
Index page
<?php defined('BASEPATH') OR exit('No direct script access allowed'); ?>
<div class="login-box">
<div class="login-logo">
<a href="<?= base_url();?>"><b>STS</b> CPanel</a>
</div>
<?php if($this->session->flashdata('msg') != ''){?>
<div class="callout callout-danger">
<h4>Login Failure</h4>
<?php echo $this->session->flashdata('msg');?>
</div><?php }?>
<div class="login-box-body">
<fieldset>
<form action="<?= base_url();?>" method="post" autocomplete="off">
<div class="form-group has-feedback">
<input type="text" name="email" class="form-control" placeholder="Login ID">
<span class="glyphicon glyphicon-lock form-control-feedback"></span>
</div>
<div class=" has-feedback">
<input type="password" name="password" class="form-control"
placeholder="Login PIN">
<span class="glyphicon glyphicon-pushpin form-control-feedback"></span>
</div>
<button type="submit" class="btn btn-success btn-block btn-flat">Sign
In</button>
</form>
</fieldset>
</div>
</div>
DASHBOARD
<?php
defined('BASEPATH') OR exit('No direct script access allowed');
$this->load->view('themes/navbar');
$this->load->view('themes/sidebar');
?> <section class="content-header">
<h1> Dashboard
</h1> <ol class="breadcrumb">
<li><a href="#"><i class="fa fa-dashboard"></i> Home</a></li>
<li class="active">Dashboard</li> </ol>
</section>

<section class="content"> <div class="row">
<div class="col-md-12"> <div class="row">
<div class="col-md-6"> </div>
</div> <div class="box box-info">
<div class="box-header with-border"> <h3 class="box-title">Site
Back Up</h3> <div class="box-tools pull-right">
<button class="btn btn-box-tool" data-widget="collapse"><i class="fa fa-
minus"></i></button>
<button class="btn btn-box-tool" data-widget="remove"><i class="fa fa-
times"></i></button>
</div>
</div>
<div class="box">
<div class="box-header">
</div>
<form method="POST" action="<?= base_url('backend/home/error');?>">
<div class="box-body">
<table id="datatables" class="table table-bordered table-striped">
<thead>
<tr>
<th width="1%" align="left"><input type="checkbox" name="chkall"
id="chkall" onclick="return checkall('selector[]');"> </th>
<th>Error Type</th>
<th>Error Title</th>
<th>Location</th>
<th>Error Line</th>
<th>Time</th>
</tr>
</thead>
<tbody>
<?php foreach($error_log as $key => $value):
echo '<tr>
<td width="1%"><input type="checkbox" name="selector[]"
id="selector[]" value="'.$value->id. '"/>
<td>'.$value->errtype.'</td>

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