By the same authors

Coevolution of Intelligent Agents using Cartesian Genetic Programming

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Author(s)

Department/unit(s)

Publication details

Title of host publicationGECCO 2007: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2
DatePublished - 2007
Pages269-276
Number of pages8
PublisherASSOC COMPUTING MACHINERY
Place of PublicationNEW YORK
Original languageEnglish
ISBN (Print)978-1-59593-697-4

Abstract

A coevolutionary competitive learning environment, for two antagonistic agents is presented. The agents are controlled by a new kind of computational network based oil a compartmentalised model of neurons. The genetic basis of neurons is an important [27] and neglected aspect of previous approaches. Accordingly, we have defined a collection of chromosomes representing various aspects of the neuron soma, dendrites and axon branches, and synaptic connections Chromosomes are represented and evolved using a form of genetic programming (GP) known as Cartesian GP. The network formed by running the chromosomal programs, has a highly dynamic morphology in which neurons grow, and die, and neurite branches together with synaptic connections for and change in response to environmental interactions. The idea of this paper is to demonstrate the importance of the genetic transfer of learned experience and life time learning. The learning is a consequence of the complex dynamics produced its a result of interaction (coevolution) between two intelligent agents. Our results show that, both agents exhibit interesting learning capabilities.

    Research areas

  • Genetic Programming, Co-evolution, Brain, Artificial Neural Networks, SYNAPTIC PLASTICITY, EVOLUTION, NEUTRALITY, LANDSCAPE

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations