1993 | OriginalPaper | Buchkapitel
Efficient Parallel Learning in Classifier Systems
verfasst von : U. Hartmann
Erschienen in: Artificial Neural Nets and Genetic Algorithms
Verlag: Springer Vienna
Enthalten in: Professional Book Archive
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Classifier systems are simple production systems working on binary messages of a fixed length. Genetic algorithms are employed in classifier systems in order to discover new classifiers. We use methods of the computational complexity theory in order to analyse the inherent difficulty of learning in classifier systems. Hence our results do not depend on special (possibly genetic) learning algorithms. The paper formalises this rule discovery or learning problem for classifier systems which has been proved to be hard in general. It will be proved that restrictions on two distinct learning problems lead to problems in NC, i.e. problems which are efficiently solvable in parallel.