A surrogate-assisted and informed linkage aware GA
Pages 1467 - 1468
Abstract
We present a genetic algorithm that combines linkage learning, surrogate models and informed operators. Linkage learning aims at measuring and exploiting interdependence of groups of genes. Surrogate models are fitness approximators to ease the task of calculating true fitness values. Informed operators generate, evaluate and rank a set of solutions according to their fitness model to return their most fit solution. Our described approach provides on-line perturbation based linkage learning and informed linkage exploitation with novel, specialized operators. Results of experimental runs on several synthetic fitness function compositions are provided to demonstrate significant improvement of the final result quality compared to a conventional GA setup.
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Index Terms
- A surrogate-assisted and informed linkage aware GA
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July 2012
1586 pages
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Association for Computing Machinery
New York, NY, United States
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Published: 07 July 2012
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GECCO '12
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GECCO '12: Genetic and Evolutionary Computation Conference
July 7 - 11, 2012
Pennsylvania, Philadelphia, USA
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