The Evolution of Connectionist Networks

Xin YAO

doi:10.1007/978-94-017-0793-0_16

The Evolution of Connectionist Networks

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Abstract

Learning and evolution are two fundamental processes of adaptation. Various models have been proposed to explain their behaviour. Rather than discussing these models in detail, this paper concentrates on the interaction between learning and evolution as well as the interaction between different levels of evolution. We will argue that the evolution of learning rules and its interaction with other evolutionary developments (in either artificial or biological systems) plays a key role in accounting for the creativity of those systems. We will concentrate on two models of learning and evolution: connectionistlearning (artificial neural networks, or ANNs) and genetic algorithms (GAs).

Original language	English
Title of host publication	Artificial Intelligence and Creativity: An Interdisciplinary Approach
Editors	Terry DARTNALL
Publisher	Springer Dordrecht
Chapter	16
Pages	233-243
Number of pages	11
ISBN (Electronic)	9789401707930
ISBN (Print)	9789048144570
DOIs	https://doi.org/10.1007/978-94-017-0793-0_16
Publication status	Published - 1994
Externally published	Yes

Publication series

Name	Studies in Cognitive Systems
Publisher	Springer Dordrecht
Volume	17
ISSN (Print)	0924-0780

Keywords

Genetic Algorithm
Learning Rule
Connection Weight
Binary String
Crossover Operation

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10.1007/978-94-017-0793-0_16

Cite this

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abstract = "Learning and evolution are two fundamental processes of adaptation. Various models have been proposed to explain their behaviour. Rather than discussing these models in detail, this paper concentrates on the interaction between learning and evolution as well as the interaction between different levels of evolution. We will argue that the evolution of learning rules and its interaction with other evolutionary developments (in either artificial or biological systems) plays a key role in accounting for the creativity of those systems. We will concentrate on two models of learning and evolution: connectionistlearning (artificial neural networks, or ANNs) and genetic algorithms (GAs).",

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AB - Learning and evolution are two fundamental processes of adaptation. Various models have been proposed to explain their behaviour. Rather than discussing these models in detail, this paper concentrates on the interaction between learning and evolution as well as the interaction between different levels of evolution. We will argue that the evolution of learning rules and its interaction with other evolutionary developments (in either artificial or biological systems) plays a key role in accounting for the creativity of those systems. We will concentrate on two models of learning and evolution: connectionistlearning (artificial neural networks, or ANNs) and genetic algorithms (GAs).

KW - Genetic Algorithm

KW - Learning Rule

KW - Connection Weight

KW - Binary String

KW - Crossover Operation

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The Evolution of Connectionist Networks

Abstract

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Keywords

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