A chaotic approach to maintain the population diversity of genetic algorithm in network training.
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.
Lü
Qingzhang
Q
State Key laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, 410082, Changsha, People's Republic of China. rqyu@hnu.net.cn
Shen
Guoli
G
Yu
Ruqin
R
D000465:Algorithms; D016208:Databases, Factual; D014644:Genetic Variation; D005828:Genetics, Population; D008957:Models, Genetic; D009154:Mutation; D016571:Neural Networks, Computer