A Hybrid Immune Genetic Algorithm to Solve University Time Table Problems - BOONYOPAKORN, Pongsarun; MEESAD, Phayung (Walailak Journal of Science and Technology (WJST), 2017)
Timetabling problems are complex optimization tasks which can be difficult to solve, especially when trying to achieve the optimal results. Moreover, they are also known to be NP-hard (Non-deterministic Polynomial-time hard) problems. Usually, problems are defined as assigning a set of lectures to a fixed number of timeslots and rooms such that they satisfy a number of hard and soft constraints. Genetic algorithm (GA) is an approach to optimize learning based on the principles of biological evolution. In the past, GA have been applied to solve instances of timetabling which are combined with various techniques and optimization operators to optimal realistic requirements. In this paper, a new genetic hybrid algorithm which includes an artificial immune system called the Immune Genetic Algorithm (IGA) is proposed. This method mainly aims to construct a feasible timetabling which satisfies all constraints as best as possible. The main idea of this paper is to reduce the number of initial chromosomes to a local search space. In addition, the author has also compared the results with Genetic Algorithm (GA), Immune Algorithm (IA), and Practical Swarm Optimization (PSO) to illustrate that the IGA produces good quality solutions and outperforms similar methods.