A chaotic approach to maintain the population diversity of genetic algorithm in network training

Authors:
Qingzhang Lü;Guoli Shen;Ruqin Yu
Affiliations:
State Key laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, People's Republic of China;State Key laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, People's Republic of China;State Key laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, People's Republic of China
Venue:
Computational Biology and Chemistry
Year:
2003

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Abstract

The concept of chaos being radically different from statistical randomness is introduced into chemometrics research. The chaotic system that is deterministic with underlying patterns and inherent ability in searching the space of interest has been employed to improve the performance of chemometric algorithms. In this paper, a chaotic mutation is introduced into the genetic algorithm (GA) which is used for artificial neural network (ANN) training. The chaotic algorithm is very efficient in maintaining the population diversity during the evolution process of GA. The proposed algorithm CGANN has been testified by prediction of vibrational frequencies of octahedral hexahalides from some selected molecular parameters.