title: Genetic training of network using chaos concept: application to QSAR studies of vibration modes of tetrahedral halides.
abstract: The chaotic dynamical system is introduced in genetic algorithm to train ANN to formulate the CGANN algorithm. Logistic mapping as one of the most important chaotic dynamic mappings provides each new generation a high chance to hold GA's population diversity. This enhances the ability to overcome overfitting in training an ANN. The proposed CGANN has been used for QSAR studies to predict the tetrahedral modes (nu(1)(A1) and nu(2)(E)) of halides [MX(4)](epsilon). The frequencies predicted by QSAR were compared with those calculated by quantum chemistry methods including PM3, AM1, and MNDO/d. The possibility of improving the predictive ability of QSAR by including quantum chemistry parameters as feature variables has been investigated using tetrahedral tetrahalide examples.
authors.affiliation: State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, People's Republic of China.
authors.forename: Qingzhang
authors.initials: Q
authors.lastname: Lu
authors.forename: Guoli
authors.initials: G
authors.lastname: Shen
authors.forename: Ruqin
authors.initials: R
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D055598:Chemical Phenomena; D002627:Chemistry, Physical; D016208:Databases, Factual; D005828:Genetics, Population; D006219:Halogens / Q000737:chemistry*; D008962:Models, Theoretical
chemical_list: D006219:Halogens