An epistasis measure based on the analysis of variance for the real-coded representation in genetic algorithms

Abstract

Epistasis is a measure of interdependence between genes and an indicator of problem difficulty in genetic algorithms. Many researches have concentrated on the epistasis measure in binary coded representation in genetic algorithms. However, a few attempts for epistasis measure in real-coded representation have been reported in the literature. In this paper, we have demonstrated how to use the approach of analysis of variance (ANOVA) to estimate the epistasis in real-coded representation. The approach is useful to analyse epistasis in genetic algorithms in a more detailed level. Examples have been given for showing how to use ANOVA for measuring the amount of epistasis in parametrical problems, and then we have applied this epistatic information provided by ANOVA to improve the performance of genetic algorithm.