An Optimised Evolutionary Algorithms for Nonparameter Penalty Function
Yuren Zhou · Jisuanji gongcheng · 2005
Penalty functions are often used in constrained optimization, however it is difficult to chose parameter properly. This paper introduces a new nonparameter penalty function for evolutionary optimization. It puts dynamic penalty to constraint violations, and balances feasible solutions and infeasible solutions through fitness. This makes group approach optimal solution easily. The new algorithm is tested using multi-parent crossover evolution strategy on 5 benchmark problems.Results show that the new method is efficient ,robust and easy to realize.