Evolution Strategies for Constrained Optimization

Evolution strategies (ESs), especially versions of the so-called covariance matrix adaptation ES (CMA-ES), are arguably the currently best-performing general purpose direct search methods for unconstrained optimization of real-parameter black-box optimization problems as often encountered in simulation-based optimization and other fields of engineering optimization. However, up until now, the success of these direct search strategies is rather restricted to the unconstrained case. That is, the incorporation of equality and inequality constraints in the design of ESs is still in an infant state when compared to other classes of Evolutionary Algorithms such as Differential Evolution. It is the goal of this project to foster the development of ESs for constrained optimization based on a theoretically-grounded basis. This will be accomplished by a closely coupled research program that connects theoretically motivated algorithm design, analysis, and evaluation of the direct search strategies developed. Being based on the knowledge gained through this research, a deeper understanding of the working principles of such search strategies in constrained search spaces is expected. This will not only lead to better performing ESs, but also to general design principles for Evolutionary Algorithms.

No additional funding sources recorded.