Polygene-based evolutionary algorithms with frequent pattern mining

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Wang:2018:FCS,

• author = "Shuaiqiang Wang and Yilong Yin",
• title = "Polygene-based evolutionary algorithms with frequent pattern mining",
• journal = "Frontiers of Computer Science",
• year = "2018",
• volume = "12",
• number = "5",
• pages = "950--965",
• month = oct,
• keywords = "genetic algorithms, genetic programming, PGEA, polygenes, evolutionary algorithms, function optimization, associative classification data mining",
• ISSN = "2095-2228",
• URL = "https://link.springer.com/article/10.1007/s11704-016-6104-3",
• DOI = "doi:10.1007/s11704-016-6104-3",
• size = "16 pages",
• abstract = "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.",
• notes = "Is this GP?

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

  Supplementary material is available for this article at https://doi.org/10.1007/s11704-016-6104-3 and is accessible for authorized users.",

}

Genetic Programming entries for Shuaiqiang Wang Yilong Yin

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