In this paper, we study the conditions in which the (1+1)-EA compares favorably to other evolutionary algorithms (EAs) in terms of fitness function distribution at given iteration and with respect to the average optimization time. Our approach is applicable when the reproduction operator of an evolutionary algorithm is dominated by the mutation operator of the (1+1)-EA. In this case one can extend the lower bounds obtained for the expected optimization time of the (1+1)-EA to other EAs based on the dominated reproduction operator. This method is exampled on the sorting problem with HAM landscape and the exchange mutation operator. We consider several simple examples where the (1+1)-EA is the best possible search strategy in the class of the EAs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.