A Novel Genetic Algorithm with Orthogonal Prediction for Global Numerical Optimization

Authors:
Jun Zhang;Jing-Hui Zhong;Xiao-Min Hu
Affiliations:
Department of Computer Science, SUN Yat-sen University, Guangzhou, China 510275;Department of Computer Science, SUN Yat-sen University, Guangzhou, China 510275;Department of Computer Science, SUN Yat-sen University, Guangzhou, China 510275
Venue:
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Year:
2008

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Abstract

This paper proposes a novel orthogonal predictive local search (OPLS) to enhance the performance of the conventional genetic algorithms. OPLS operation predicts the most promising direction for the individuals to explore their neighborhood. It uses the orthogonal design method to sample orthogonal combinations to make the prediction. The resulting algorithm is termed the orthogonal predictive genetic algorithm (OPGA). OPGA has been tested on eleven numerical optimization functions in comparison with some typical algorithms. The results demonstrate the effectiveness of the proposed algorithm for achieving better solutions with a faster convergence speed.