An Empirical Investigation of Optimum Tracking with Evolution Strategies.

This article reports the results of a thorough empirical examination concerning the parameter settings in optimum tracking with (1 +λ) evolution strategies. The investigated scenario is derived from real-world applications where the evaluation function is very expensive concerning the computation time. This is modeled by a strong dependence of the dynamics on the number of evaluations. This model enables the derivation of optimal population sizes that might serve as recommendations for general applications. Both random and linear dynamics and two modes for correlating population size and strength of dynamics are examined. The results show for mere tracking problems that plus selection outperforms comma selection and that self-adaptive evolution strategies are able to deliver a close to optimal performance. [ABSTRACT FROM AUTHOR]