A Review of Constraint-Handling Techniques for Evolution Strategies

Evolution strategies are successful global optimization methods. In many practical numerical problems constraints are notexplicitly given. Evolution strategies have to incorporate techniques to optimize in restricted solution spaces. Famous constraint-handling techniques are penalty and multiobjective approach es. Past work has shown that in particular an ill-conditionedalignment between the coordinate system of Gaussian mutation and the constraint boundaries leads to premature convergence.Covariance matrix adaptation evolution strategies offer a solution to this alignment problem. Last, metamodeling of the constraintboundary leads to significant savings of constraint function calls and to a speedup by repairing infeasible solutions. This work givesa brief overview over constraint-handling methods for evolution strategies by demonstrating the approaches experimentally ontwo exemplary constrained problems.