Local SVM Constraint Surrogate Models for Self-adaptive Evolution Strategies

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.