标题:Polygene-based evolutionary algorithms with frequent pattern mining
作者:Wang, Shuaiqiang; Yin, Yilong
作者机构:[Wang, Shuaiqiang] Qilu Univ Technol, Res Ctr Big Data Applicat, Jinan 250100, Shandong, Peoples R China.; [Wang, Shuaiqiang] Univ Jyvaskyla, Dept C 更多
通讯作者:Wang, SQ;Wang, SQ;Wang, Shuaiqiang
通讯作者地址:[Wang, SQ]Qilu Univ Technol, Res Ctr Big Data Applicat, Jinan 250100, Shandong, Peoples R China;[Wang, SQ]Univ Jyvaskyla, Dept Comp Sci & Informat Sys 更多
来源:FRONTIERS OF COMPUTER SCIENCE
出版年:2018
卷:12
期:5
页码:950-965
DOI:10.1007/s11704-016-6104-3
关键词:polygenes; evolutionary algorithms; function optimization; associative; classification; data mining
摘要:In this paper, we introduce polygene-based evolution, a novel framework for evolutionary algorithms (EAs) that features distinctive operations in the evolutionary process. In traditional EAs, the primitive evolution unit is a gene, wherein genes are independent components during evolution. In polygene-based evolutionary algorithms (PGEAs), the evolution unit is a polygene, i.e., a set of co-regulated genes. Discovering and maintaining quality polygenes can play an effective role in evolving quality individuals. Polygenes generalize genes, and PGEAs generalize EAs. Implementing the PGEA framework involves three phases: (I) polygene discovery, (II) polygene planting, and (III) polygene-compatible evolution. For Phase I, we adopt an associative classification-based approach to discover quality polygenes. For Phase II, we perform probabilistic planting to maintain the diversity of individuals. For Phase III, we incorporate polygene-compatible crossover and mutation in producing the next generation of individuals. Extensive experiments on function optimization benchmarks in comparison with the conventional and state-of-the-art EAs demonstrate the potential of the approach in terms of accuracy and efficiency improvement.
收录类别:EI;SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045155424&doi=10.1007%2fs11704-016-6104-3&partnerID=40&md5=7e05d553d4005d581e217a1fcd82450a
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