A novel selection mechanism for evolutionary algorithms with metameric variable-length representations

作     者：Ryerkerk, Matt Averill, Ron Deb, Kalyanmoy Goodman, Erik 

作者机构：Michigan State Univ E Lansing MI 48824 USA 

出 版 物：《SOFT COMPUTING》 (Soft Comput.)

年 卷 期：2020年第24卷第21期

页      面：16439-16452页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Science Foundation [DBI-0939454] Defense Advanced Research Projects Agency (DARPA) Michigan State University 

主　　题：Metameric representations Evolutionary algorithms Variable-length algorithms 

摘      要：Metameric problems are variable-length optimization problems whose representations take on an at least partially segmented structure. This is referred to as a metameric representation. Frequently, each of these segments defines one of a number of analogous components in the solution. Examples include the nodes in a coverage network or turbines in a wind farm. Locating optimal solutions requires, in part, determining the optimal number of components. Evolutionary algorithms can be applied but require modifications to the traditional fixed-length operators. This study proposes a new selection operator for metameric problems: length niching selection. First, the population is partitioned into several niches based on solution length. A window function determines at which lengths a niche is formed. Local selection is then applied within each niche independently, resulting in a new parent population formed by a diverse set of solution lengths. A coverage and a wind farm problem are used to demonstrate the effectiveness of the new operator.