Continuous Selection and Self-Adaptive Evolution Strategies

The intention of this work is to eliminate the need for a synchronous generation scheme in the (μ +, λ) evolution strategy. It is motivated by the need for a more practical implementation of selection strategies on parallel machine architectures. This strategy is known as continuous or steady state selection. Continuous selection is known to reduce significantly the number of function evaluations needed to reach an optimum, in evolutionary search, for some problems. Here evolution strategy theory is used to illustrate when continuous selection is more efficient than generational selection. How this gain in efficiency may influence the overall effectiveness of the evolution strategy is also investigated. The implementation of continuous selection becomes problematic for algorithms using explicitly encoded selfadaptive strategy parameters. Self-adaption is therefore given special consideration in this work. The discussion leads a new evolution strategy version.