Abstract genetic representation of dynamical neural networks using Kauffman networks.
Abstract (developmental) genetic representations are schemes where each genotype encodes a program for the construction of a phenotype. A new method for contriving abstract genetic representations is presented, based upon Kauffman's ideas regarding biological development [13-16]. Phenogenesis is controlled by genotype via cell replication and differentiation. Comparison is made with the earlier published methods of Gruau [9] and Kitano [18]. It is argued that greater expressive power is obtained using Kauffman networks. The new method was tested in the artificial evolution of morphology and finally successfully applied to the synthesis of structure in dynamical neural networks.
East
I R
IR
Department of Computer Science, De Montfort University, Milton Keynes, Great Britain. Ian.East@dial.pipex.com
Rowe
J
J
D019143:Evolution, Molecular; D008957:Models, Genetic; D016571:Neural Networks, Computer