Synthesis of sigma-pi neural networks by the breeder genetic programming

Genetic programming has been successfully applied to evolve computer programs for solving a variety of interesting problems. The breeder genetic programming (BGP) method has Occam´s razor in its fitness measure to evolve minimal size multilayer perceptrons. In this paper, we apply the method to synthesis of sigma-pi neural networks. Unlike perceptron architectures, sigma-pi networks use product units as well as summation units to build higher-order terms. The effectiveness of the method is demonstrated on benchmark problems. Simulation results on noisy data suggest that BGP not only improves the generalization performance, but it can also accelerate the convergence speed