Vol. 4, No. 4 April 2013 ISSN 2079-8407 Journal of Emerging Trends in Computing and I nformation Sciences ©2009-2013 CIS Journal. All rights reserved. http://www.cisjournal.org 436 On the Fitness Measure of Genetic Algorithm for Generating Institutional Lecture Timetable 1 M. O. Odim, 2 B. O. Oguntunde, 3 O. O. Alli 1, 2 Department of Mathematical Sciences, Redeemer’s University, Nigeria 3 Oyo State Universal Basic Education Board, Ibadan, Nigeria ABSTRACT We conducted a study on the performance of genetic algorithm in designing institutional lecture time table, using empirical data of a college in a University. The study was focused on assessing the effectiveness of the algorithm given a number of hard constraints and a limited number of resources. The algorithm was implemented in C++. Several tests representing different scenarios were run and we found that genetic algorithm would always search for an optimum lecture allocation that satisfies the hard constraints in generating Institutional lecture time table. The algorithm can only reach the fitness of 1 when all the hard constraints have been satisfied. The fitness of the algorithm can be improved upon by the provision of adequate number and sufficient capacity of resources to carter for the hard constraints. We therefore, conclude that genetic algorithm works best in an environment where resources capacity and availability do not constitute some bottleneck. Keywords: Fitness Measure, Genetic algorithm, lecture timetable and Hard constraints 1. INTRODUCTION Timetabling is non-trivial matter; it has been an issue of major concern to researchers. Lecture timetabling is an NP-hard combinatorial optimization problem, which lacks analytical solution methods. It has been the major concern of various research fields such as Artificial Intelligence and Operations Research. Over the years, several researchers have proposed a number of approaches to solving the timetabling problem. Some of the approaches include genetic algorithm, constraints Logic programming, simulating annealing, graph coloring, conventional programs, expert system, fuzzy genetic heuristic and a number of others. Each of the approaches has their strengths and limitations. The study was focused on assessing the effectiveness of the genetic algorithm given a number of hard constraints and a limited number of resources. A lecture timetable problem is concerned with finding the exact time allocation within limited time period of number of events (courses-lectures) and assigning to them number of resources (teachers, students and Lecture Halls) while satisfying some constraints. The constraints are classified into Hard Constraints and Soft constraints. Hard constraints are those that must be adhered to, while soft Constraints can be violated if necessary [1, 2]. The advantage of GA is that they can explore the solution space in multiple directions at once [3]. Therefore, 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. In the paper we presented our finding on the performance of GA for designing and generating Institutional lecture timetable, while satisfying pre-defined hard constraints. The rest of the paper is organized as follows: section 2 presents a review of related work. In section 3, we present our methodology. Section discussed the results and we give our concluding remark in section 5 2. RELATED WORK Genetic Algorithms (GAs) have been applied to many scientific, engineering, business and entertainment, and a wide variety of optimization tasks, including numerical optimization, and combinatorial optimization problems such as Traveling Salesman Problem [4], Job Shop Scheduling [5], Lecture timetabling [6], Examination timetabling [7] and Video & Sound quality optimization [8]. It has been also used in automatic programming to evolve computer programs for specific tasks, and to design other computational structures, for example, cellular automata and sorting networks [9]. GAs has as well been used for many machine-learning applications, including classification and prediction, and protein structure prediction. It has also been used to design neural networks, to evolve rules for learning classifier systems or symbolic production systems, and to design and control robots. There are also some applications of GA in economic model such modeling of processes of innovation, the development of bidding strategies, and the emergence of economic markets. It equally has been used to model various aspects of the natural immune system, including somatic mutation during an individual lifetime and the discovery of multi- gene families during evolutionary time [8]. Automated timetabling has been an issue of major concern to researchers. in our society. In August 1995, an international conference was held on the Practice and Theory of Automated Timetabling (PATAT). The aim of the conference was to align the needs of practitioners and the objectives of researchers through presentation and application of leading edge research techniques. The success of this conference brought about the formation of a committee named EURO (European Conference on Operations Research) Working Group on Automated timetabling (WATT) [10,