Abstract
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 that adjusts the crossover rate and frequency of mutation of each individual, and adopts the gradient of the individual to decide their mutation value. Traditional mathematical problems and an electromagnetic benchmark are solved using niching genetic algorithms to show their interest in real world optimization.
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