caa a22 4500 467952361 CHVBK 20180323165703.0 cr unu---uuuuu 170328e19881001xx s 000 0 eng 10.1023/A:1022658204162 doi (NATIONALLICENCE)springer-10.1023/A:1022658204162 (NATIONALLICENCE)springer-10.1007/BF00113895 A Tale of Two Classifier Systems [Elektronische Daten] [George Robertson, Rick Riolo] This paper describes two classifier systems that learn. These are rule-based systems that use genetic algorithms, which are based on an analogy with natural selection and genetics, as their principal learning mechanism, and an economic model as their principal mechanism for apportioning credit. CFS-C is a domain-independent learning system that has been widely tested on serial computers. CFS is a parallel implementation of CFS-C that makes full use of the inherent parallelism of classifier systems and genetic algorithms, and that allows the exploration of large-scale tasks that were formerly impractical. As with other approaches to learning, classifier systems in their current form work well for moderately-sized tasks but break down for larger tasks. In order to shed light on this issue, we present several empirical studies of known issues in classifier systems, including the effects of population size, the actual contribution of genetic algorithms, the use of rule chaining in solving higher-order tasks, and issues of task representation and dynamic population convergence. We conclude with a discussion of some major unresolved issues in learning classifier systems and some possible approaches to making them more effective on complex tasks. Kluwer Academic Publishers, 1988 Classifier systems nationallicence genetic algorithms nationallicence parallelism nationallicence sequence prediction nationallicence Robertson George Thinking Machines Corporation, 245 First Street, 02142, Cambridge, MA, U.S.A. aut Riolo Rick Xerox Palo Alto Research Center, 3333 Coyote Hill Road, 94304, Palo Alto, CA, U.S.A. aut Machine Learning Kluwer Academic Publishers-Plenum Publishers 3/2-3(1988-10-01), 139-159 0885-6125 3:2-3<139 1988 3 10994 https://doi.org/10.1023/A:1022658204162 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1023/A:1022658204162 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Riolo Rick Xerox Palo Alto Research Center, 3333 Coyote Hill Road, 94304, Palo Alto, CA, U.S.A aut NATIONALLICENCE 773 0- Machine Learning Kluwer Academic Publishers-Plenum Publishers 3/2-3(1988-10-01), 139-159 0885-6125 3:2-3<139 1988 3 10994 NATIONALLICENCE 700 1- Robertson George Thinking Machines Corporation, 245 First Street, 02142, Cambridge, MA, U.S.A aut NATIONALLICENCE 856 40 https://doi.org/10.1007/BF00113895 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Riolo Rick Electrical Engineering & Computer Science Department, University of Michigan, 48109, Ann Arbor, MI, U.S.A aut NATIONALLICENCE 773 0- Machine Learning Kluwer Academic Publishers 3/2-3(1988-10-01), 139-159 0885-6125 3:2-3<139 1988 3 10994 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE NATIONALLICENCE NL-springer