Performing Classification with an Environment Manipulating Mutable Automata (EMMA)

Created by W.Langdon from gp-bibliography.bib Revision:1.8154

@InProceedings{benson:2000:PCEMMA,

• author = "Karl Benson",
• title = "Performing Classification with an Environment Manipulating Mutable Automata (EMMA)",
• booktitle = "Proceedings of the 2000 Congress on Evolutionary Computation CEC00",
• year = "2000",
• pages = "264--271",
• address = "La Jolla Marriott Hotel La Jolla, California, USA",
• publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
• month = "6-9 " # jul,
• organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)",
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming, system modelling and control, EMMA algorithm, classification, complex decision space, environment manipulating mutable automata, feature selection, finite state automata, hypersurface discriminant functions, object detection, symbiotic architecture, finite state machines, object detection, pattern classification",
• ISBN = "0-7803-6375-2",
• DOI = "doi:10.1109/CEC.2000.870305",
• size = "8 pages",
• abstract = "In this paper a novel approach to performing classification is presented. Hypersurface Discriminant functions are evolved using Genetic Programming. These discriminant functions reside in the states of a Finite State Automata, which has the ability to reason 1 and logically combine the hypersurfaces to generate a complex decision space. An object may be classified by one or many of the discriminant functions, this is decided by the automata. During the evolution of this symbiotic architecture, feature selection for each of the discriminant functions is achieved implicitly, a task which is normally performed before a classification algorithm is trained. Since each dis-criminant function has different features, and objects may be classified with one or more discriminant functions, no two objects from the same class need be classified using the same features. Instead, the most appropriate features for a given object are used.",
• notes = "CEC-2000 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE.

  IEEE Catalog Number = 00TH8512,

  Library of Congress Number = 00-018644

  ",

}

Genetic Programming entries for Karl A Benson

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