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Human-Competitive Lens System Design with Evolution Strategies

Christian Gagné, Julie Beaulieu, Marc Parizeau and Simon Thibault
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Abstract - Lens system design provides ideal problems for evolutionary algorithms: a complex non-linear optimization task, often with intricate physical constraints, for which there is no analytical solutions. This paper demonstrates, through the use of two evolution strategies, namely non-isotropic Self-Adaptive evolution strategy (SA-ES) and Covariance Matrix Adaptation evolution strategy (CMA-ES), as well as multiobjective Non-Dominated Sort Genetic Algorithm 2 (NSGA-II) optimization, the human competitiveness of an approach where an evolutionary algorithm is hybridized with a local search algorithm to solve both a classic benchmark problem, and a real-world problem.

Bibtex:

@article{Gagné741,
author    = { Christian Gagné and Julie Beaulieu and Marc Parizeau and Simon Thibault },
title     = { Human-Competitive Lens System Design with Evolution Strategies },
volume    = { 8 },
number    = { 4 },
pages     = { 1439-1452 },
year      = { 2008 },
month     = { September },
journal   = { Applied Soft Computing },
keywords  = { Lens design; Global optimization; Evolutionary computation; Evolution strategies; Multiobjective optimization; Memetic algorithm; Human competitiveness },
web       = { http://dx.doi.org/10.1016/j.asoc.2007.10.018 }
}

Dernière modification: 2008/09/02 par cgagne

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