Linkage learning, overlapping building blocks, and systematic strategy for scalable recombination | Zendy

Tian–Li Yu | Zendy; Kumara Sastry | Zendy; David E. Goldberg | Zendy

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Linkage learning, overlapping building blocks, and systematic strategy for scalable recombination

Author(s) -

Tian–Li Yu,

Kumara Sastry,

David E. Goldberg

Publication year - 2005

Publication title -

citeseer x (the pennsylvania state university)

Language(s) - English

Resource type - Conference proceedings

ISBN - 1-59593-010-8

DOI - 10.1145/1068009.1068209

Subject(s) - crossover , linkage (software) , scalability , convergence (economics) , computer science , set (abstract data type) , genetic algorithm , recombination , operator (biology) , field (mathematics) , theoretical computer science , artificial intelligence , mathematics , machine learning , genetics , biology , repressor , database , transcription factor , pure mathematics , economics , gene , programming language , economic growth

This paper aims at an important, but poorly studied area in genetic algorithm (GA) field: How to design the crossover operator for problems with overlapping building blocks (BBs). To investigate this issue systematically, the relationship between an inaccurate linkage model and the convergence time of GA is studied. Specifically, the effect of the error of so-called false linkage is analogized to a lower exchange probability of uniform crossover. The derived qualitative convergence-time model is used to develop a scalable recombination strategy for problems with overlapping BBs. A set of problems with circularly overlapping BBs exemplify the recombination strategy.

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