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Frontiers of Computer Science

, Volume 12, Issue 5, pp 950–965 | Cite as

Polygene-based evolutionary algorithms with frequent pattern mining

  • Shuaiqiang Wang
  • Yilong Yin
Research Article
  • 22 Downloads

Abstract

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.

Keywords

polygenes evolutionary algorithms function optimization associative classification data mining 

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Notes

Acknowledgments

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 (U1201258), the National Natural Science Foundation of China (Grant Nos. 71402083, 61573219, 61502258) and the National Science Foundation of Shandong Province (ZR2014FQ007).

Supplementary material

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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Research Center of Big Data ApplicationsQilu University of TechnologyJinanChina
  2. 2.Department of Computer Science and Information SystemsUniversity of JyvaskylaJyvaskylaFinland
  3. 3.School of Computer Science and TechnologyShandong UniversityJinanChina

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