Meta-lamarckian learning in memetic algorithms
Meta-lamarckian learning in memetic algorithms
Over the last decade, memetic algorithms (MAs) have relied on the use of a variety of different methods as the local improvement procedure. Some recent studies on the choice of local search method employed have shown that this choice significantly affects the efficiency of problem searches. Given the restricted theoretical knowledge available in this area and the limited progress made on mitigating the effects of incorrect local search method choice, we present strategies for MA control that decide, at runtime, which local method is chosen to locally improve the next chromosome. The use of multiple local methods during a MA search in the spirit of Lamarckian learning is here termed Meta-Lamarckian learning. Two adaptive strategies for Meta-Lamarckian learning are proposed in this paper. Experimental studies with Meta-Lamarckian learning strategies on continuous parametric benchmark problems are also presented. Further, the best strategy proposed is applied to a real-world aerodynamic wing design problem and encouraging results are obtained. It is shown that the proposed approaches aid designers working on complex engineering problems by reducing the probability of employing inappropriate local search methods in a MA, while at the same time, yielding robust and improved design search performance.
adaptive meta-lamarckian learning, continuous
parametric design optimization, hybrid genetic algorithm-local
search (ga-ls), memetic algorithm (ma)
99-110
Ong, Yew Soon
3e7a6a91-6eab-4ca6-81c5-c9f3ee20e2fb
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
2004
Ong, Yew Soon
3e7a6a91-6eab-4ca6-81c5-c9f3ee20e2fb
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Ong, Yew Soon and Keane, A.J.
(2004)
Meta-lamarckian learning in memetic algorithms.
IEEE Transactions on Evolutionary Computation, 8 (2), .
(doi:10.1109/TEVC.2003.819944).
Abstract
Over the last decade, memetic algorithms (MAs) have relied on the use of a variety of different methods as the local improvement procedure. Some recent studies on the choice of local search method employed have shown that this choice significantly affects the efficiency of problem searches. Given the restricted theoretical knowledge available in this area and the limited progress made on mitigating the effects of incorrect local search method choice, we present strategies for MA control that decide, at runtime, which local method is chosen to locally improve the next chromosome. The use of multiple local methods during a MA search in the spirit of Lamarckian learning is here termed Meta-Lamarckian learning. Two adaptive strategies for Meta-Lamarckian learning are proposed in this paper. Experimental studies with Meta-Lamarckian learning strategies on continuous parametric benchmark problems are also presented. Further, the best strategy proposed is applied to a real-world aerodynamic wing design problem and encouraging results are obtained. It is shown that the proposed approaches aid designers working on complex engineering problems by reducing the probability of employing inappropriate local search methods in a MA, while at the same time, yielding robust and improved design search performance.
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ong_04.pdf
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Published date: 2004
Keywords:
adaptive meta-lamarckian learning, continuous
parametric design optimization, hybrid genetic algorithm-local
search (ga-ls), memetic algorithm (ma)
Identifiers
Local EPrints ID: 22794
URI: http://eprints.soton.ac.uk/id/eprint/22794
PURE UUID: 45d3189c-3e3b-47fc-9169-6d39981b7e46
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Date deposited: 22 Mar 2006
Last modified: 16 Mar 2024 02:53
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Author:
Yew Soon Ong
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