The Evolution of Connectionist Networks

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).KeywordsGenetic AlgorithmLearning RuleConnection WeightBinary StringCrossover OperationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.