Evolved cooperation and emergent communication structures in learning classifier based organic computing systems

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Evolved cooperation and emergent communication structures in learning classifier based organic computing systems

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Genetic And Evolutionary Computation Conference archive
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers table of contents

Montreal, Québec, Canada

WORKSHOP SESSION: International workshop on learning classifier systems table of contents

Pages 2633-2640

Year of Publication: 2009

ISBN:978-1-60558-505-5

Authors

Alexander Scheidler	University of Leipzig, Leipzig, Germany
Martin Middendorf	University of Leipzig, Leipzig, Germany

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

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ABSTRACT

In this paper we look at systems consisting of many autonomous components or agents which have only limited amount of resources (e.g. memory) but are able to communicate with each other. The aim of these systems is to solve classification problems (usually to classify binary strings). We incorporate a pittsburgh style learning classifier system into the agents and extend its possible actions by actions for passing the classification requests to other agents. We show that the system is able to overcome the limited resources of its parts by evolving cooperation between them. We take a deeper look at the structure of the generated rule sets and investigate the occurring communication patterns.

REFERENCES

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