Parameter tuned CMA-ES on the CEC´15 expensive problems

Evolutionary optimization algorithms have parameters that are used to adapt the search strategy to suit different optimization problems. Selecting the optimal parameter values for a given problem is difficult without a-priori knowledge. Experimental studies can provide this knowledge by finding the best parameter values for a specific set of problems. This knowledge can also be constructed into heuristics (rule-of-thumbs) that can adapt the parameters for the problem. The aim of this paper is to assess the heuristics of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimization algorithm. This is accomplished by tuning CMA-ES parameters so as to maximize its performance on the CEC´15 problems, using a bilevel optimization approach that searches for the optimal parameter values. The optimized parameter values are compared against the parameter values suggested by the heuristics. The difference between specialized and generalized parameter values are also investigated.