Reasoning and learning method for fuzzy rules using neural networks with adaptive structured genetic algorithm

In this paper, we present a reasoning and learning method for fuzzy rules using neural networks with adaptive structured genetic algorithm. This adaptive structured genetic algorithm can determine the network structure and their weights solely by an evolutionary process. 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 adaptive structured genetic algorithm can generate or annihilate the specified units respectively in hidden layer to achieve an overall good system, without using back propagation or any other learning algorithm.