Population set-based global optimization algorithms: some modi(cations and numerical studies

This paper studies the e.ciency and robustness of some recent and well known population set-based direct search global optimization methods such as Controlled Random Search, Di4erential Evolution and the Genetic Algorithm. Some modi(cations are made to Di4erential Evolution and to the Genetic Algorithm to improve their e.ciency and robustness. All methods are tested on two sets of test problems, one composed of easy but commonly used problems and the other of a number of relatively di.cult problems.