A Stationary Point Convergence Theory for Evolutionary Algorithms.
William E. Hart · 1996
This paper defines a class of evolutionary algorithms called evolutionary pattern search algorithms (EPSAs) and analyzes their convergence properties. This class of algorithms is closely related to evolutionary programming, evolution strategie and real-coded genetic algorithms. EPSAs are self-adapting evolutionary algorithms that modify the step size of the mutation operator in response to the success of previous optimization steps. The rule used to adapt the step size can be used to provide a stationary point convergence theory for EPSAs on any continuous function. This convergence theory is based on an extension of the convergence theory for generalized pattern search methods. 1 Introduction This paper concerns the application of evolutionary search algorithms to solve an unconstrained minimization problem to find x 2 D such that f(x ) = min x2D f(x)