Evaluation of Mutation Strategy Performance in Memetic Algorithm

Abstract: Memetic algorithm generates solutions to optimization problems using techniques inspired by natural evolution like genetic algorithm and local search. Our motivation is the application of evolutionary algorithms for solving real-world optimization problems. In this work, a new concept called gene tuning is introduced which associates with mutation for solving the multi objective soft constrained combinatorial problems. Also, various strategies of mutation regarding the selection of soft constraints are introduced and investigated. To achieve this, experiments are conducted on course timetabling problem. The discussion on the experimental results gives an indication towards promising mutation for practical application.

Keywords: Genetic Algorithm, Genetic Operators, Combinatorial Optimization, Mutation, Crossover.