Study of improved hierarchy genetic algorithm based on adaptive niches

Abstract

Canonical genetic algorithms have the defects of pre-maturity and stagnation when applied in optimizing problems. In order to avoid the short-comings, an adaptive niche hierarchy genetic algorithm (ANHGA) is proposed. The algorithm is based on the adaptive mutation operator and crossover operator to adjust the crossover rate and probability of mutation of each individual, whose mutation values are decided using individual gradient. This approach is applied in Percy and Shubert function optimization. Comparisons of niche genetic algorithm (NGA), hierarchy genetic algorithm (HGA) and ANHGA have been done by establishing a simulation model and the results of mathematics model and actual industrial model show that ANHGA is feasible and efficient in the design of multi-extremum. © Springer-Verlag Berlin Heidelberg 2005.