Title :
Exploring the effects of Lamarckian and Baldwinian learning in evolving recurrent neural networks
Author :
Ku, Kim W C ; Mak, M.W.
Author_Institution :
Dept. of Electron. Eng., Hong Kong Polytech. Univ., Hong Kong
Abstract :
A drawback of using genetic algorithms (GAs) to train recurrent neural networks is that it takes a large number of generations to evolve the networks into an optimal solution. In order to reduce the number of generations taken, the Lamarckian learning mechanism and the Baldwinian learning mechanism are embedded into a cellular GA. This paper investigates the effects of these two learning mechanisms on the convergence performance of the cellular GA. The criteria that make learning useful to GAs are also discussed. The results show that the Lamarckian mechanism is able to assist the cellular GA, while the Baldwinian mechanism fails to do so. In addition to reducing the number of generations taken, we have found that it is also possible to reduce the time taken by embedding learning into the cellular GA in an appropriate manner
Keywords :
cellular automata; convergence; genetic algorithms; learning (artificial intelligence); optimisation; recurrent neural nets; Baldwinian learning; Lamarckian learning; backpropagation; cellular genetic algorithm; convergence performance; evolving recurrent neural networks; genetic algorithms; neural network training; optimal solution; time; Backpropagation algorithms; Biological cells; Cellular networks; Genetic algorithms; Genetic mutations; Intelligent networks; Learning systems; Neural networks; Recurrent neural networks; Stochastic processes;
Conference_Titel :
Evolutionary Computation, 1997., IEEE International Conference on
Conference_Location :
Indianapolis, IN
Print_ISBN :
0-7803-3949-5
DOI :
10.1109/ICEC.1997.592386
