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Frontiers of Computer Science

Front. Comput. Sci.    2018, Vol. 12 Issue (5) : 950-965
Polygene-based evolutionary algorithms with frequent pattern mining
Shuaiqiang WANG1,2(), Yilong YIN3()
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
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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.

Keywords polygenes      evolutionary algorithms      function optimization      associative classification      data mining     
Corresponding Authors: Shuaiqiang WANG   
Just Accepted Date: 19 October 2016   Online First Date: 06 March 2018    Issue Date: 21 September 2018
 Cite this article:   
Shuaiqiang WANG,Yilong YIN. Polygene-based evolutionary algorithms with frequent pattern mining[J]. Front. Comput. Sci., 2018, 12(5): 950-965.
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Shuaiqiang WANG
Yilong YIN
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