A Computational Framework for Indirect Encoding Methods ofEvolutionary Neural Networks
Weiyi Wei · Dianzi xuebao · 2013
According to the difficulties in the evolving large scale neural networks using the traditional direct encoding methods,many researchers are proposing the novel indirect encoding methods for evolutionary neural networks.That is,a network structure that repeats many times can be represented by a single set of genes that is reused in mapping from genotype to phenotype,and such genetic reuse allows searching the large scale neural networks through a lower dimensional genotypic space.In this paper,we introduce a general computational framework for the indirect encoding methods of evolutionary neural networks through the study of existed methods,in which every evolutionary process of neural networks is divided into three stages:development,learning and evolution.Additionally,we analyze the advantages and disadvantages for the different indirect encoding methods from two aspects of the computational framework:genome evolution and neural network development.