On the behavior of evolutionary algorithms in dynamic environments

Abstract

The capability of evolution strategies and evolutionary programming to track the optimum in simple dynamic environments is investigated for different types of dynamics, update frequencies, and displacement strengths. Experimental results are reported for a (15100)-evolution strategy with lognormal self-adaptation, a standard evolutionary programming algorithm with multiplicative self-adaptation rule, and an evolutionary programming algorithm with lognormal self-adaptation. The evolution strategy and lognormal evolutionary programming prove their capability to track the dynamic optimum, while evolutionary programming with multiplicative self-adaptation rule fails in the dynamic environment. These results support the conclusions that the choice of a particular mechanism for self-adaptation can be critical in dynamic environments, and the lognormal rule utilized in evolution strategies is well suited for such kind of problems.