Optimal Genetic Representation of Complete Strictly-Layered Feedforward Neural Networks

Abstract

The automatic evolution of neural networks is both an attractive and a rewarding task. The connectivity matrix is the most common way of directly encoding a neural network for the purpose of genetic optimization. However, this representation presents several disadvantages mostly stemming from its inherent redundancy and its lack of robustness. We propose a… (More)
DOI: 10.1007/3-540-45723-2_15

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