The investigation includes the computation of the maximal performance of an ES using recombination can reach when the mutation strength is optimally adjusted during the whole evolution. The comparison between the strategies with and without sSA shows that self-adaptation is not always able to drive the ES in its optimal working regime, although it still guarantees linear convergence order. The static and dynamic aspects of self-adaptation are discussed and it is shown that the learning parameter has a sensible influence on the progress rate. The self-adaptation of the mutation distribution is a distinguishing feature of evolutionary algorithms that optimize over continuous variables. It is widely recognized that self-adaptation accelerates the search for optima and enhances the ability to locate optima accurately, but it is generally unclear whether these optima are global or not.
Tournaments naturally emphasize survival.
However, a natural opposite of survival is extinction, and a study of the fossil record shows extinction plays a key role in the evolutionary process. This paper presents a new evolutionary algorithm that emphasizes extinction to conduct search operations over a fitness landscape. In previous work we have analyzed the empirical performance of EPSAs. This paper revisits that analysis and extends it to a more general model of mutation. We experimentally evaluate how the choice of the set of mutation offsets affects optimization performance for EPSAs.