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Learning classifier systems to evolve classification rules for systems of memory constrained components

par Scheidler, Alexander ;Middendorf, Martin
Référence Evolutionary Intelligence, 4, 3, page (127-143)
Publication Publié, 2011-09

Article révisé par les pairs

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Résumé :

In this paper we study how to solve classification problems in computing systems that consist of distributed, memory constrained components. Interacting Pittsburgh-style Learning Classifier Systems are used to generate sets of classification rules that can be deployed on the components. We show that this approach distributes the knowledge and enables the components to solve complex classification problems in cooperation. We study the structure and properties of the evolved rule sets and analyse the way the components share their knowledge. Moreover, we investigate the influence of different communication topologies and the introduction of communication costs on the emerging patterns of cooperation and on the classification performance of the whole system. © 2011 Springer-Verlag.