title: Genetic evolution of the topology and weight distribution of neural networks.
abstract: This paper proposes a system based on a parallel genetic algorithm with enhanced encoding and operational abilities. The system, used to evolve feedforward artificial neural networks, has been applied to two widely different problem areas: Boolean function learning and robot control. It is shown that the good results obtained in both cases are due to two factors: first, the enhanced exploration abilities provided by the search-space reducing evolution of both coding granularity and network topology, and, second, the enhanced exploitational abilities due to a recently proposed cooperative local optimizing genetic operator.
authors.affiliation: Dipartimento Elettronica e Informazzione, Politecnico di Milano.
authors.forename: V
authors.initials: V
authors.lastname: Maniezzo