Polygene-based evolutionary algorithms with frequent pattern mining

Shuaiqiang Wang

doi:10.1007/s11704-016-6104-3

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Polygene-based evolutionary algorithms with frequent pattern mining

2018-02-07

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Shuaiqiang Wang *

尹义龙 (Yilong Yin)

DOI：10.1007/s11704-016-6104-3

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Abstract

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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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