Polygene-based evolution: a novel framework for evolutionary algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Shuaiqiang Wang and Byron J. Gao and 
                 Shuangling Wang and Guibao Cao and Yilong Yin",
  title =        "Polygene-based evolution: a novel framework for
                 evolutionary algorithms",
  booktitle =    "21st ACM International Conference on Information and
                 Knowledge Management, CIKM'12",
  year =         "2012",
  editor =       "Xue-wen Chen and Guy Lebanon and Haixun Wang and 
                 Mohammed J. Zaki",
  pages =        "2263--2266",
  address =      "Maui, HI, USA",
  month =        oct # " 29 - " # nov # " 2",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4503-1156-4",
  DOI =          "doi:10.1145/2396761.2398616",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  size =         "4 pages",
  abstract =     "In this paper, we introduce polygene-based evolution,
                 a novel framework for evolutionary algorithms (EAs)
                 that features distinctive operations in the evolution
                 process. In traditional EAs, the primitive evolution
                 unit is gene, where genes are independent components
                 during evolution. In polygene-based evolutionary
                 algorithms (PGEAs), the evolution unit is polygene,
                 i.e., a set of co-regulated genes. Discovering and
                 maintaining quality polygenes can play an effective
                 role in evolving quality individuals. Polygenes
                 generalise genes, and PGEAs generalize EAs.
                 Implementing the PGEA framework involves three phases:
                 polygene discovery, polygene planting, and
                 polygene-compatible evolution. Extensive experiments on
                 function optimisation benchmarks in comparison with the
                 conventional and state-of-the-art EAs demonstrate the
                 potential of the approach in accuracy and efficiency
  notes =        "No mention of GP

Genetic Programming entries for Shuaiqiang Wang Byron J Gao Shuangling Wang Guibao Cao Yilong Yin