Optimization of modular neural networks with fuzzy integration using genetic algorithms applied to pattern recognition

We described in this paper the evolution of modular neural networks using hierarchical genetic algorithms. Modular neural networks (MNN) have shown significant learning improvement over single neural networks (NN). For this reason, the use of MNN for pattern recognition is well justified. However, network topology design of MNN is at least an order of magnitude more difficult than for classical NNs. We described in this paper the use of a hierarchical genetic algorithm (HGA) for optimizing the topology of each of the neural network modules of the MNN. Simulation results shown in this paper proved the feasibility and advantages of the proposed approach.