A Simple Approach for Constrained Optimization - An Evolution Strategy that Evolves Rays

This paper applies an evolution strategy (ES) that evolves rays to single-objective real-valued constrained optimization problems. The algorithm is called Ray-ES. It was proposed as an ad hoc optimization approach for dealing with the unconstrained real-parameter optimization problem class called HappyCat. To our knowledge, the application of the Ray-ES to constrained problems is new. It serves as a simple alternative to other approaches such as for example differential evolution (DE). This paper describes how the Ray-ES can be applied to a constrained setting. The algorithm is tested on a variety of different test problems. Additionally, it is compared to DE approaches.

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