Increasing Robustness Of Genetic Algorithm

Genetic algorithms are often well suited for optimization problems because of their parallel searching and evolutionary ability. Crossover and mutation are believed to be the main exploration operators in GA. In this paper, we focus on how crossover and mutation work in GA and investigate their effect on bit's frequency of the population. To increase robustness against uncertainty of GA, a new recombination method based on bit's frequency of the population and a new robust generation strategy were proposed. The proposed methods were tested on the problem with many local minima. Simulation results demonstrate the effectiveness of the proposed methods.