模糊自适应多细胞GEP函数优化算法

Seft-adaptive multicellular GEP algorithm based on fuzzy control for function optimization

为提高传统GEP算法的全局搜索能力,提出一种基于模糊控制的多细胞基因表达式编程算法(multicellular GEP algorithm based on fuzzy control,MGEP-FC)。通过构建模糊隶属函数,对算法的交叉率、变异率和实数集变异率的大小进行描述,根据种群中个体适应度值的集中和分散程度,动态调整遗传操作的交叉率、变异率和实数集变异率。为使种群的多样性在迭代过程中得到延续,设计一种遗传操作方案,将产生的新个体与父代种群结合构建临时种群,临时种群和子代种群的多样性均得到优化。12个Benchmark的函数寻优实验结果表明,该算法在稳定性、全局收敛能力和寻优速度等方面都得到了显著提升。

To improve the global optimization ability of traditional GEP algorithm,a multicellular gene expression programming algorithm based on fuzzy control(multicellular GEP algorithm based on fuzzy control,MGEP-FC)was proposed.The sizes of cross rate,mutation rate and real number mutation rate were described by constructing fuzzy membership function.According to the concentration and dispersion of individual fitness values in population,the crossover rate,mutation rate and real number set mutation rate of genetic operation were dynamically adjusted.To make the diversity of the population continue in the iterative process,agenetic operation scheme was designed,which combined the new individuals with the parent population to build a temporary population,and the diversities of the temporary and subpopulation were optimized.The results of 12 Benchmark optimization experiments show that the MGEP-FC algorithm is greatly improved in stability,global convergence and optimization speed.