An improved hierarchy genetic algorithm based on adaptive niches

Canonical genetic algorithms have the defects of pre-maturity and stagnation when applied in optimizing problems. In order to avoid the shortcomings, 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 frequency of mutation of each individual whose mutation values are decided using individual gradient. This approach is used in Percy and Shubert function optimization. Comparisons between NGA and ANHGA have been done by establishing a simulation model, the results of mathematics model and actual industrial model show that ANHGA is feasible and efficient in the design of multi-extremum.