Classifier systems and genetic algorithms
dc.contributor.author | Booker, Lashon B. (Lashon Bernard) | en_US |
dc.contributor.author | Goldberg, David E. | en_US |
dc.contributor.author | Holland, John Henry | en_US |
dc.date.accessioned | 2006-04-07T20:42:29Z | |
dc.date.available | 2006-04-07T20:42:29Z | |
dc.date.issued | 1989-09 | en_US |
dc.identifier.citation | Booker, L. B., Goldberg, D. E., Holland, J. H. (1989/09)."Classifier systems and genetic algorithms." Artificial Intelligence 40(1-3): 235-282. <http://hdl.handle.net/2027.42/27777> | en_US |
dc.identifier.uri | http://www.sciencedirect.com/science/article/B6TYF-47X29WK-33/2/e5caf52050c77b8bd71babce471f27a7 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/27777 | |
dc.description.abstract | Classifier systems are massively parallel, message-passing, rule-based systems that learn through credit assignment (the bucket brigade algorithm) and rule discovery (the genetic algorithm). They typically operate in environments that exhibit one or more of the following characteristics: (1) perpetually novel events accompanied by large amounts of noisy or irrelevant data; (2) continual, often real-time, requirements for action; (3) implicitly or inexactly defined goals; and (4) sparse payoff or reinforcement obtainable only through long action sequences. Classifier systems are designed to absorb new information continuously from such environments, devising sets of competing hypotheses (expressed as rules) without disturbing significantly capabilities already acquired. This paper reviews the definition, theory, and extant applications of classifier systems, comparing them with other machine learning techniques, and closing with a discussion of advantages, problems, and possible extensions of classifier systems. | en_US |
dc.format.extent | 2933573 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Elsevier | en_US |
dc.title | Classifier systems and genetic algorithms | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Science (General) | en_US |
dc.subject.hlbsecondlevel | Computer Science | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Computer Science and Engineering, 3116 EECS Building, The University of Michigan, Ann Arbor, MI 48109, U.S.A. | en_US |
dc.contributor.affiliationum | Computer Science and Engineering, 3116 EECS Building, The University of Michigan, Ann Arbor, MI 48109, U.S.A. | en_US |
dc.contributor.affiliationum | Computer Science and Engineering, 3116 EECS Building, The University of Michigan, Ann Arbor, MI 48109, U.S.A. | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/27777/1/0000171.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1016/0004-3702(89)90050-7 | en_US |
dc.identifier.source | Artificial Intelligence | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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