中山大学学报（自然科学版）2012，Vol.51Issue(1)：50-54,5.
一种改进的微种群遗传算法
An Improved Micro-genetic Algorithm
摘要
Abstract
Population isolation, arithmetic crossover and optimum reserved strategy are used to improve micro-genetic algorithm ( mGA). Reset frequency is decreased while the global and local searching capabilities of mGA between two resets are enhanced, which makes mGA searching the parameter space intelligently as the mode recognition information is preserved as much as possible. Real-code is used to decrease the computing cost in encoding and decoding. Adaptive random mutation with existing genetic information of the current groups is used to increase efficient search. Heterogeneous strategy is used to improve the probability of convergence to global optimal solution and quicken up the convergence. Finally , standard functions testing demonstrate that the improved mGA can find better optimum solutions with less computing cost than standard genetic algorithm (SGA).关键词
微种群遗传算法/异种机制/自适应非均匀变异/算数交叉/实数编码Key words
micro genetic algorithm/ heterogeneous strategy/ adaptive random mutation/ arithmetic cross/ real code分类
建筑与水利引用本文复制引用
燕乐纬,陈洋洋,周云..一种改进的微种群遗传算法[J].中山大学学报（自然科学版）,2012,51(1):50-54,5.基金项目
国家自然科学基金资助项目(11102045) （11102045）
广东省自然科学基金博士启动资助项目(S2011040004039) （S2011040004039）
广东省高校优秀青年创新资助项目(LYM10108) （LYM10108）
