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一种极值导向的自适应演化规划

A Self-adaptive evolutionary programming based on optimum search direction
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摘要 传统的演化规划(CEP)依赖高斯变异算子,而快速演化规划(FEP)选择柯西分布作为主要的变异算子。改进的快速演化规划(IFEP)是将柯西变异算子和高斯变异算子的搜索倾向混合起来。每个父代生成两个子代,一个有柯西变异算子,另一个有高斯变异算子,然后比较这两个子代,将变现好的一个保留作为下一代。在本文,我们提出了一种极值导向的自适应变异算子演化规划(OSDEP),它的基本思想是将当前最优搜索方向引入柯西变异算子中,在OSDEP中每个个体在柯西变异算子作用下,再沿着当前最优解的方向进行搜索。大量的数值试验对OSDEP,IFEP,FEP和CEP进行比较。从这些具有广泛代表性的七个测试函数的数值试验结果,我们可以观察到对于单峰函数、有少数局部最优的多峰函数和有很多局部最优的多峰函数DSEP比IFEP,FEP和CEP都要表现好。 The Classical Evolutionary Programming (CEP) relies on Gaussian mutation, whereas Fast Evolutionary Programming (FEP) selects Cauchy distribution as the primary mutation operator, Improved Fast Evolutionary (IFEP) selects the better Gaussian and Cauchy distribution as the primary mutation operator. In this paper, we propose a self-adaptive Evolutionary Programming base on Optimum Search Direction (OSDEP) in which we introduce the current best global individual into mutation to guide individuals to converge according to the global search direction. Extensive empirical studies have been carried out to evaluate the performance of OSDEP, IFEP, FEP and CEP. From the experimental results on seven widely used test functions, we can show that OSDEP outperforms all of IFEP, FEP and CEP for all the test functions.
出处 《深圳信息职业技术学院学报》 2015年第1期11-15,共5页 Journal of Shenzhen Institute of Information Technology
基金 广东省自然基金项目(S20130014108) 深圳市科技计划项目(JCYJ20130401095559825 JC201006020807A) 深圳市经济信息委员会项目(20130806094356)
关键词 演化规划 高斯变异 柯西变异 极值导向自适应变异 evolntionary programming gaussion mutation cauchy mutation optimum search direction adaptive mutation
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