An Improved Fuzzy Genetic Algorithm to Suppress the Premature Convergence

Aiming at the premature convergence of the genetic algorithm, an improved fuzzy genetic algorithm is proposed. In this algorithm, the mean square deviation of group fitness and population evolution generation are used as the criteria of prema- ture convergence, and according to the estimation from fuzzy logic controllers, relevant evolution methods are given to different chromosomes, that is punishing the strongers and awarding the weakers when the population evolves normally in order to maintain the diversity of population, while doing catastrophe operation to the weakers to renew population evolution once the premature convergence appears or tends to appear. The experiment results show that the improved fuzzy genetic algorithm can maintain the population diversity and suppress the premature convergence better in comparison with the standard genetic algo- rithm, the adaptive genetic algorithm and the fuzzy genetic algorithm.