Polygene-based evolution: a novel framework for evolutionary algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{DBLP:conf/cikm/WangGWCY12,

• 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 improvement.",
• notes = "No mention of GP http://www.cikm2012.org/accepted_papers.php",

}

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

Citations