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
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.