Evaluation of novel adaptive evolutionary programming on four constraint handling techniques

This paper presents empirical studies carried out to evaluate the performance of different constraint handling methods on constrained real-parameter optimization using a novel adaptive evolutionary programming (EP). 25 runs have been conducted for each of the 13 test problems considered. Our experimental results show that no single constraint handling method can be the best for all problems i.e, each constraint handling method is suitable only for a subset of problems. We also show that the novel adaptive EP proposed in this paper has improved performance over the classical EP (CEP).

[1]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[2]  Xin Yao,et al.  Stochastic ranking for constrained evolutionary optimization , 2000, IEEE Trans. Evol. Comput..

[3]  Xin Yao,et al.  Fast Evolutionary Programming , 1996, Evolutionary Programming.

[4]  Zbigniew Michalewicz,et al.  Evolutionary Algorithms for Constrained Parameter Optimization Problems , 1996, Evolutionary Computation.

[5]  Xin Yao,et al.  Adapting Self-Adaptive Parameters in Evolutionary Algorithms , 2001, Applied Intelligence.

[6]  Carlos Artemio Coello-Coello,et al.  Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art , 2002 .

[7]  A. Kai Qin,et al.  Self-adaptive Differential Evolution Algorithm for Constrained Real-Parameter Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[8]  Gary G. Yen,et al.  A Self Adaptive Penalty Function Based Algorithm for Constrained Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[9]  Xin Yao,et al.  Scaling Up Evolutionary Programming Algorithms , 1998, Evolutionary Programming.

[10]  Tetsuyuki Takahama,et al.  Constrained Optimization by the ε Constrained Differential Evolution with Gradient-Based Mutation and Feasible Elites , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[11]  K. Deb.  An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[12]  Jing J. Liang,et al.  Problem Deflnitions and Evaluation Criteria for the CEC 2006 Special Session on Constrained Real-Parameter Optimization , 2006 .

[13]  David B. Fogel,et al.  System Identification Through Simulated Evolution: A Machine Learning Approach to Modeling , 1991 .

[14]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .