Title :
Determination and adaptive alteration of artificial neural network structures by a genetic algorithm with a controlled genotype-phenotype mapping
Author :
Murata, Junichi ; Tanaka, Kei ; Hirasawa, Kotaro
Author_Institution :
Dept. of Electr. & Electron. Syst. Eng., Kyushu Univ., Fukuoka, Japan
Abstract :
A method is proposed for determination and adaptive alteration of artificial neural network (ANN) structures. Not only the weights but also the structure is altered adaptively. From the engineering viewpoint, such an adaptation will be beneficial, for example, for accommodation of faults which require different structures of the ANN. The salient point of the proposed method is that it can alter the ANN structure by adjusting a single parameter; it is not necessary to consider which particular nodes are to be removed or where and how many nodes are to be added. The method is based on genetic algorithms (GA) which emulate evolution. A fitness function and a coding system of ANN structures on chromosomes are also proposed which are appropriate for optimization of the ANN structures
Keywords :
adaptive systems; encoding; feedforward neural nets; genetic algorithms; learning (artificial intelligence); neural net architecture; optimisation; chromosomes; coding system; evolution; feedforward neural network; fitness function; genetic algorithm; genotype-phenotype mapping; neural network structures; Adaptive control; Artificial neural networks; Biological cells; Chromosome mapping; Control systems; Genetic algorithms; Genetic engineering; Programmable control; Supervised learning; Systems engineering and theory;
Conference_Titel :
Systems, Man, and Cybernetics, 1996., IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-3280-6
DOI :
10.1109/ICSMC.1996.565357