Genetic evolution of the topology and weight distribution of neural networks

Authors:
V. Maniezzo
Affiliations:
Dipartimento Elettronica e Informazzione, Politecnico di Milano
Venue:
IEEE Transactions on Neural Networks
Year:
1994

Citing 0
Cited 17

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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