Evolutionary Optimization of the Structure of Neural Networks by a Recursive Mapping as Encoding
The determination of the appropriate structure of artificial neural networks for a specific problem or problem domain remains an open question. One attempt to solve this optimization problem is the application of evolutionary algorithms and the choice of an appropriate coding, the genotype → phenotype mapping. We employ a coding procedure from the class of recursive coding methods and apply the optimization process to the problem of prediction and modeling of chaotic time series. The network structure and the inital weight setting are determined by an evolutionary process and the ‘fine tuning’ of weights is achieved by a standard back-propagation algorithm. We focus on the properties of the coding procedure and the understanding of the network structures in this context.
KeywordsEvolutionary Algorithm Small Chromosome Large Chromosome Learning Cycle Chaotic Time Series
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