Function optimization research based on Evolutionary Programming

Evolutionary Programming (EP) is a kind of stochastic optimization algorithm. The goal of EP is to achieve intelligent behavior through simulated evolution. EP algorithms are based on an arbitrarily initialized population of search points which evolves towards better and better regions in the search space by means of randomized process of mutation and selection. To avoid premature convergence and balancing the ability of exploration and exploitation has become one of the important aspects of EP´s study. We describe the classic evolutionary programming (CEP) which is the basic algorithm of evolutionary programming. FEP improved CEP by replacing the Gaussian mutation in CEP by Cauchy mutation. The main focus of this thesis is several EP algorithms which are introduced in detail and studied.