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Polygene-based evolutionary algorithms with frequent pattern mining

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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.

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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).

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Authors and Affiliations

  1. Research Center of Big Data Applications, Qilu University of Technology, Jinan, 250100, China

    Shuaiqiang Wang

  2. Department of Computer Science and Information Systems, University of Jyvaskyla, Jyvaskyla, 40014, Finland

    Shuaiqiang Wang

  3. School of Computer Science and Technology, Shandong University, Jinan, 250101, China

    Yilong Yin

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  1. Shuaiqiang Wang
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  2. Yilong Yin
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Correspondence to Shuaiqiang Wang.

Additional information

A preliminary version of this paper was published in the Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM) [1]

Shuaiqiang Wang received the PhD and BS degrees in computer science from Shandong University, China in 2009 and 2004 respectively. Currently he is an assistant professor at University of Jyvaskyla, Finland. Before that, He was an associate professor at Shandong University of Finance and Economics, China from 2011 to 2014, and a postdoctoral research associate at Texas State University, USA in 2010. His research interests include information retrieval and data mining.

Yilong Yin is a professor of computer science and the director of the MLA Lab in Shandong University, China. He received his PhD degree from Jilin University, China in 2000. From 2000 to 2002, he worked as a post-doctoral fellow in the Department of Electronic Science and Engineering, Nanjing University, China. His research interests include machine learning, data mining, computational medicine and biometrics.

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Wang, S., Yin, Y. Polygene-based evolutionary algorithms with frequent pattern mining. Front. Comput. Sci. 12, 950–965 (2018). https://doi.org/10.1007/s11704-016-6104-3

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