Presentation 2006-06-16
Fast Method of Genetic Algorithm Searching and its Application to Neural Network Training
Moriyoshi MAEHSIRO, Hiroshi KINJO, Kunihiko NAKAZONO, Tetsuhiko YAMAMOTO,
PDF Download Page PDF download Page Link
Abstract(in Japanese) (See Japanese page)
Abstract(in English) Genetic algorithm (GA) is known to be one of the most powerful solution searching mechanism for nonlinear and multi-variable optimization problems. Generally, GA takes many long times to find the solutions and sometimes it cannot find the optimum solutions. In order to improve the searching performance, we propose a fast algorithm of GA and a mutation method. The fast algorithm is usage of a momentum offspring (MOS). The MOS is a individual not the crossover but by the best individuals between current and past generation. The MOS is considered it has higher probability for desired solution and the effect of MOS is fast searching of the optimum solution. Furthermore we proposed a constant range mutation (CRM) for the GA. The CRM is considered it has an effect of avoiding the ineffective individual production. We apply the GA with MOS and CRM to neural network training. Simulation shows proposed method has good training performances.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Genetic algorithm / Fast algorithm / Momentum offspring / Constant range mutation / Neural network training
Paper # NC2006-36
Date of Issue

Conference Information
Committee NC
Conference Date 2006/6/9(1days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair
Vice Chair
Secretary
Assistant

Paper Information
Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Fast Method of Genetic Algorithm Searching and its Application to Neural Network Training
Sub Title (in English)
Keyword(1) Genetic algorithm
Keyword(2) Fast algorithm
Keyword(3) Momentum offspring
Keyword(4) Constant range mutation
Keyword(5) Neural network training
1st Author's Name Moriyoshi MAEHSIRO
1st Author's Affiliation Graduate School of Mechanical Engineering, University of the Ryukyus()
2nd Author's Name Hiroshi KINJO
2nd Author's Affiliation University of the Ryukyus
3rd Author's Name Kunihiko NAKAZONO
3rd Author's Affiliation University of the Ryukyus
4th Author's Name Tetsuhiko YAMAMOTO
4th Author's Affiliation University of the Ryukyus
Date 2006-06-16
Paper # NC2006-36
Volume (vol) vol.106
Number (no) 102
Page pp.pp.-
#Pages 4
Date of Issue