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Experimental Validations of the Learnable Evolution Model

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dc.contributor.author Cervone, Guido
dc.contributor.author Kaufman, Kenneth A.
dc.contributor.author Michalski, Ryszard S.
dc.date.accessioned 2006-11-03T18:17:06Z
dc.date.available 2006-11-03T18:17:06Z
dc.date.issued 2000-07 en_US
dc.identifier.citation Cervone, G., Kaufman, K. and Michalski, R. S., "Experimental Validations of the Learnable Evolution Model," 2000 Congress on Evolutionary Computation, San Diego CA, pp 1064-1071, July 2000. en_US
dc.identifier.uri https://hdl.handle.net/1920/1467
dc.description.abstract A recently developed approach to evolutionary computation, called Learnable Evolution Model or LEM, employs machine learning to guide processes of generating new populations. The central new idea of LEM is that it generates new individuals by processes of hypothesis generation and instantiation, rather than by mutation and/or recombination, as in conventional evolutionary computation methods. The hypotheses are generated by a machine learning program from examples of high and low performance individuals. When applied to problems of function optimization and parameter estimation for nonlinear filters, LEM significantly outperformed the evolutionary computation algorithms used in experiments, sometimes achieving two or more orders of magnitude of evolution speed-up in terms of the number of generations (or births). An application of LEM to the problem of optimizing heat exchangers has produced designs equal to or exceeding the best human designs.
dc.format.extent 1916 bytes
dc.format.extent 834632 bytes
dc.format.extent 260991 bytes
dc.format.mimetype text/xml
dc.format.mimetype application/postscript
dc.format.mimetype application/pdf
dc.language.iso en_US en_US
dc.relation.ispartofseries P 00-9 en_US
dc.title Experimental Validations of the Learnable Evolution Model en_US
dc.type Presentation en_US


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