Title :
Genetic evolution of the topology and weight distribution of neural networks
Author :
Maniezzo, Vittorio
Author_Institution :
Dipartimento Elettronica e Informazzione, Politecnico di Milano, Italy
fDate :
1/1/1994 12:00:00 AM
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
Keywords :
Boolean functions; feedforward neural nets; genetic algorithms; learning (artificial intelligence); network topology; parallel algorithms; robots; search problems; Boolean function learning; coding granularity; cooperative local optimizing genetic operator; exploitational abilities; exploration abilities; feedforward artificial neural networks; genetic evolution; neural networks; parallel genetic algorithm; robot control; search-space; topology; weight distribution; Algorithm design and analysis; Artificial neural networks; Backpropagation algorithms; Boolean functions; Computer networks; Encoding; Genetic algorithms; Network topology; Neural networks; Testing;
Journal_Title :
Neural Networks, IEEE Transactions on
