Active Sets for Explicitly Constrained Evolutionary Optimization

Active-set approaches are commonly used in algorithms for constrained numerical optimization. We propose that active-set techniques can beneficially be employed for evolutionary black-box optimization with explicit constraints and present an active-set evolution strategy. We experimentally evaluate its performance relative to those of several algorithms for constrained optimization and find that the active-set evolution strategy compares favourably for the problem set under consideration.

Automated summary

This paper proposes an active-set evolution strategy for evolutionary black-box optimization with explicit constraints, and experimentally demonstrates its advantage over other algorithms on the problem set.