Using response surface design to determine the optimal parameters of genetic algorithm and a case study

Abstract

Genetic algorithms (GAs) are efficient stochastic search techniques for approximating optimal solutions within complex search spaces and used widely to solve NP-hard problems. Genetic algorithm includes a number of parameters whose different levels strictly affect the performance of the algorithm. The general approach to determine the appropriate parameter combination of GA depends on too many trials of different combinations, and the best one of them that produces good results is selected for the programme, which would be used for problem solving. A few researchers studied on the parameter optimisation of GA. In this paper, response surface-dependent parameter optimisation is proposed to determine the optimal parameters of GA. Results are tested for benchmark problems that are most common in mixed-model assembly line balancing problems of type-I.