Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
On Evolving Social Systems: Communication, Speciation and Symbiogenesis
Computational & Mathematical Organization Theory
Machine Learning
A Coevolutionary Approach to Learning Sequential Decision Rules
Proceedings of the 6th International Conference on Genetic Algorithms
An Incremental Multiplexer Problem and Its Uses in Classifier System Research
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Organic Computing - A New Vision for Distributed Embedded Systems
ISORC '05 Proceedings of the Eighth IEEE International Symposium on Object-Oriented Real-Time Distributed Computing
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Learning classifier systems: a survey
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Improving performance in size-constrained extended classifier systems
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
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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.