GENETIC EVOLUTION OF THE TOPOLOGY AND WEIGHT DISTRIBUTION OF NEURAL NETWORKS

被引：251

作者：

MANIEZZO, V

机构：

[1] Artificial Intelligence and Robotics Project, Dip. Elettronica e informazzione, Politecnico di Milano

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 1994年 / 5卷 / 01期

关键词：

D O I：

10.1109/72.265959

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：39 / 53

页数：15