Self-Adapting Intelligent Neural Systems Using Evolutionary Techniques
Daniel Manrique (Universidad Politecnica de Madrid, Spain), Juan Rios (Universidad Politecnica de Madrid, Spain) and Alfonso Rodriguez-Paton (Universidad Politecnica de Madrid, Spain)
Copyright: © 2006
This chapter describes genetic algorithm-based evolutionary techniques for automatically constructing intelligent neural systems. These techniques can be used to build and train multilayer perceptrons with the simplest architecture. These neural networks are usually designed using binary-coded genetic algorithms. The authors show how the basic architectures codification method, which uses an algebra-based codification, employs a shorter string length and voids illegal architectures in the search space. The networks are trained using real number codification. The morphological crossover operator is presented and compared to other important real-coded crossover operators. The purpose is to understand that the combination of all these techniques results in an evolutionary system, which self-adaptively constructs intelligent neural systems to solve a problem given as a set of training patterns. To do so, the evolutionary system is applied in laboratory tests and to a real-world problem: breast cancer diagnosis.