差异进化
数学优化
CMA-ES公司
局部最优
高斯分布
非参数统计
人口
摄动(天文学)
计算机科学
协方差
适应性突变
元启发式
数学
进化算法
算法
遗传算法
进化策略
计量经济学
统计
物理
量子力学
社会学
人口学
作者
Marco Aurelio Sotelo-Figueroa,Arturo Hernández-Aguirre,Andrés Espinal,J. A. Soria-Alcaraz
出处
期刊:Studies in computational intelligence
日期:2016-12-10
卷期号:: 617-629
标识
DOI:10.1007/978-3-319-47054-2_40
摘要
Differential evolution is a population-based metaheuristic that is widely used in Black-Box Optimization. The mutation is the main search operator and there are different implementation schemes reported in state of art literature. Nonetheless, such schemes lack mechanisms for an intensification stage, which can enable better search and avoid local optima. This article proposes a way to adapt the Covariance Matrix parameter of a Gaussian distribution that is used to generate a disturbance that improves the performance of two well-known mutation schemes. This disturbance allows working with problems with correlated variables. The test was performed over the CEC 2013 instances and the results were compared through the Friedman nonparametric test.
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