Designing application-specific neural networks using the structured genetic algorithm

Presents a different type of genetic algorithm called the structured genetic algorithm (SGA) for the design of application-specific neural networks. The novelty of this new genetic approach is that it can determine the network structures and their weights solely by an evolutionary process. This is made possible for the SGA primarily due to its redundant genetic material and a gene activation mechanism which in combination provide a multi-layered structure to the chromosome. The authors focus on the use of this learning algorithm for automatic generation of a complete application specific neural network. With this approach, no a priori assumptions about topology are needed and the only information required is the input and output characteristics of the task. The empirical studies show that the SGA can efficiently determine the network size and topology along with the optimal set of connection weights appropriate for desired tasks, without using backpropagation or any other learning algorithm.<
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