An application of genetic algorithms to evolve Hopfield type optimum network architectures for object extraction
Abstract
Genetic Algorithms (GAS) have heen used l o evolve I iop j ie ld type oplimuni neural network archileciures for object background classijicatioii. Each chromosome of the GA represenis an archiieciure. The initial population is set randomly. The eirerqy t i d l l f : a i the converged state of each network is taken as its fitness. The best chromosome of I h e j i n a l generotion is taken io be the optimum network configuraiion. Tlie evolved networks are fo,iintl l o have less (compared lo t h e corresponding fixet’ fully connected version) connectivity f o r providing coniparnble oulp.uts.
Authors: Susmita De, Susmita Ghosh, Soumayadeep Pal
Published in: IEEE International Conference on Evolutionary Computation (ICEC) (2002)