Assessment of evolutionary programming models for single-objective optimization

This paper presents an assessment of different evolutionary programming (EP) techniques for solving single-objective optimization problem. Evolutionary programming has been widely used and applied with success in solving many kinds of optimization problem. However there is no benchmark to test which techniques of EP models will give a better result in solving single objective optimization. Three distinct EP models used are classical evolutionary programming (CEP), fast evolutionary programming (FEP) and improved fast evolutionary programming (IFEP). These EP techniques considered here differ in terms of search operator-Gaussian, Cauchy and mixed Gaussian-Cauchy during mutation process. Therefore, selected test functions are used as a benchmark to test which models perform better for single-objective optimization. The three EP models showed that FEP is very good in having lowest computation time and significantly better than CEP and IFEP in terms of fitness solution.