Abstract
In many applications of constrained continuous black box optimization, the evaluation of fitness and feasibility is expensive. Hence, the objective of reducing the constraint function calls remains a challenging research topic. In the past, various surrogate models have been proposed to solve this issue. In this paper, a local surrogate model of feasibility for a self-adaptive evolution strategy is proposed, which is based on support vector classification and a pre-selection surrogate model management strategy. Negative side effects suchs as a decceleration of evolutionary convergence or feasibility stagnation are prevented with a control parameter. Additionally, self-adaptive mutation is extended by a surrogate-assisted alignment to support the evolutionary convergence. The experimental results show a significant reduction of constraint function calls and show a positive effect on the convergence.
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Poloczek, J., Kramer, O. (2013). Local SVM Constraint Surrogate Models for Self-adaptive Evolution Strategies. In: Timm, I.J., Thimm, M. (eds) KI 2013: Advances in Artificial Intelligence. KI 2013. Lecture Notes in Computer Science(), vol 8077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40942-4_15
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DOI: https://doi.org/10.1007/978-3-642-40942-4_15
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