Influence of the crossover operator in the performance of the hybrid Taguchi GA

This paper investigates the influence of different crossover operators on the efficiency of the hybrid Taguchi genetic algorithm and aims to provide guidelines for algorithm's usage in continuous optimization. We examine the hybrid Taguchi genetic algorithm (HTGA) with 8 different crossover operators and apply it to 15 benchmark numerical optimization problems. The implementation uses binary representation which maps chromosomes to values in real domain with arbitrary precision. Different crossover operators are used with the HTGA and a detailed statistical analysis is performed to evaluate their performance. The results indicate that the HTGA obtains better results with crossover operators different than the ones commonly reported in literature.