Polygene-based evolutionary algorithms with frequent pattern mining  

作　　者：Shuaiqiang WANG Yilong YIN 

机构地区：[1]Research Center of Big Data Applications, Qilu University of Technology, Jinan 250100, China [2]Department of Computer Science and Information Systems, University of Jyvaskyla, Jyvaskyla 40014, Finland [3]School of Computer Science and Technology, Shandong University, Jinan 250101, China

出　　处：《Frontiers of Computer Science》2018年第5期950-965,共16页中国计算机科学前沿（英文版）

基　　金：The authors would like to thank Prof. Xin Yao for discussions and advice on this manuscript. This research was supported in part by the NSFC Joint Fund with Guangdong of China under Key Project （U 1201258）, the National Natural Science Foundation of China （Grant Nos. 71402083, 61573219, 61502258） and the National Science Foundation of Shandong Province （ZR2014FQ007）.

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 classificationbased approach to discover quality polygenes. For Phase Ⅱ, we perform probabilistic planting to maintain the diversity of individuals. For Phase Ⅲ, we incorporate polygenecompatible 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.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 classificationbased approach to discover quality polygenes. For Phase Ⅱ, we perform probabilistic planting to maintain the diversity of individuals. For Phase Ⅲ, we incorporate polygenecompatible 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.

关 键 词：polygenes evolutionary algorithms function optimization associative classification data mining 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] S512.11[自动化与计算机技术—控制科学与工程]