Chaotic Genetic AIgorithm with Feedbackand Its Applications to Constrained Optimzation

Journal of South China University of Technology (Natural Science Edition) ›› 2007, Vol. 35 ›› Issue (1): 19-23.

• Electronics, Communication & Automation Technology • Previous Articles Next Articles

Chaotic Genetic AIgorithm with Feedbackand Its Applications to Constrained Optimzation

Sun You-fa¹Gao Jing-guang¹Zhαng Cheng-ke¹Deng Fei-qi²

1. School of Economics and Management , Guangdong Univ. of Tech. , Guangzhou 510520 , Guangdong , China;2. School of Automation Science and Engineering , South China Univ. of Tech. , Guangzhou 510640 , Guangdong , China

Received:2005-08-31 Online:2007-01-25 Published:2007-01-25
Contact: 孙有发( 1976-) ，男，博士，主要从事金融系统工程、智能技术和电子商务经济学方面的研究 E-mail:youfrich@tom.com
About author:孙有发( 1976-) ，男，博士，主要从事金融系统工程、智能技术和电子商务经济学方面的研究。
Supported by:
国家自然科学基金资助项目( 60374023 ) ;广东省自然科学基金资助项目(04009475)

Abstract

Abstract:

The existing genetic algorithms are generally lost in a dilemma between prematurity and slow-convergence.In order to solve this problem , a new chaotic genetic algorithm with feedback is proposed by introducing thechaotic mapping and the posterior reinforcement leaming in the standard genetic algorithm. In this algorithm , theevolution population maintains a good diversity via the chaotic mapping , and the stochastic evolution is overcome bythe posterior reinforcement leaming based on Baldwin effect. Numerical experiments are finally carried out aimingat the complex constrained optimization problems , namely the benchmark problems. The results show that the proposedalgorithm is effective and robust.

Key words: genetic algorithm, chaos, constrained optimization, multi-objective programming, Perato dominant, Baldwin effect

Sun You-fa Gao Jing-guang Zhαng Cheng-ke Deng Fei-qi. Chaotic Genetic AIgorithm with Feedbackand Its Applications to Constrained Optimzation[J]. Journal of South China University of Technology (Natural Science Edition), 2007, 35(1): 19-23.

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Chaotic Genetic AIgorithm with Feedbackand Its Applications to Constrained Optimzation

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