一种改进的抑制早熟收敛的模糊遗传算法  

An Improved Fuzzy Genetic Algorithm to Suppress the Premature Convergence

作　　者：邓莉[1] 鲁瑞华[1] 

机构地区：[1]西南大学电子信息工程学院信号与信息处理研究所,重庆400715

出　　处：《计算机科学》2007年第11期150-153,共4页Computer Science

摘　　要：针对遗传算法中的早熟收敛现象,提出一种改进的模糊遗传算法。该算法将群体适应度均方差和种群的进化代数作为模糊逻辑控制器判断早熟收敛的标准,并根据判断结果对优劣不等的个体采取相应的进化方法,即当种群正常进化时对个体执行"惩强扶弱"的措施以保持种群多样性,一旦发生早熟收敛或有早熟收敛的趋势则对劣质个体进行局部灾变,以恢复种群的进化能力。实验结果表明,与标准遗传算法、自适应遗传算法和模糊遗传算法相比,改进的模糊遗传算法能够更好地维持种群多样性,抑制早熟收敛。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 premature 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 algorithm, the adaptive genetic algorithm and the fuzzy genetic algorithm.

关 键 词：早熟收敛 模糊遗传算法 进化代数 模糊逻辑控制器 种群多样性 

分 类 号：O242.23[理学—计算数学]