Directed Evolutionary Programming: Towards an Improved Performance of Evolutionary Programming

Evolutionary programming (EP) is one of the main classes of evolutionary algorithms (EAs). Improving existing EAs is necessary in order to achieve better results and overcome their costly computational complexity. In this paper, we present a new version of EP called Directed Evolutionary Programming (DEP) in which more directing strategies with learned termination criteria are invoked to overcome some drawbacks of EP. In DEP, the mutated children are given the chance to improve themselves with the guidance of their parents. The search process in DEP is supported by diversification and intensification schemes in order to keep the diversity, achieve faster convergence and equip the search with an automatic termination criteria. The computational experiments show that DEP is efficient and cheaper than some well-known versions of EP.