An Improved Fast Evolutionary Programming for Numerical Optimization Problems
Qiu Yuhuang · Acta Simulata Systematica Sinica · 2004
The adaptive parameters play a significant role in Evolutionary Programming (EP), which control the progress rate of the objective function in the evolutionary process. However, they are frequently lost and then make the search stagnate early or premature converge. A new mutation operator is proposed aiming at solving these problems. An extension operation is performed in order to make full use of the good mutation direction if the offspring is better than its parent. Otherwise, a Gaussian or Cauchy perturbation is superimposed on the mutation vector based on the parents performance. So the fine-tuning search ability of the Gaussian mutation and the coarse-grained search ability of the Cauchy mutation are combined efficiently. The experimental results show that the improved algorithm performs better than the classical EP for many benchmark problems.