Meta-evolutionary programming

Abstract

A brief review of efforts is simulated evolution is given. Evolutionary programming is a stochastic optimization technique that is useful for discovering the extrema of a nonlinear function. To implement such a search, several high-level parameters must be chosen, such as the amount of mutational noise, the severity of the mutation noise, and so forth. The authors address incorporating a meta-level evolutionary programming that can simultaneously evolve optimal settings for these parameters while a search for the appropriate extrema is being conducted. The preliminary experiments reported indicate the suitability of such a procedure. Meta-evolutionary programming was able to converge to points on each of two response surfaces that were close to the global optimum.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>