Recent Results from the Experimental Evaluation of the Learnable Evolution Model

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dc.contributor.authorCervone, Guido
dc.contributor.authorKaufman, Kenneth A.
dc.contributor.authorMichalski, Ryszard S.
dc.date.accessioned2006-11-03T18:17:18Z
dc.date.available2006-11-03T18:17:18Z
dc.date.issued2002-07
dc.description.abstractThe Learnable Evolution Model (LEM) represents a form of non-Darwinian evolutionary computation that is guided by a learning system. Specifically, LEM "genetically engineers" new populations via hypothesis formation and instantiation. This paper presents results from new studies in which LEM was systematically tested on a range of optimization problems and a complex real world design task. The study involved LEM2, a new implementation oriented toward function optimization, and LEMd-ISHED, an implementation oriented toward engineering design. LEM2 strongly outperformed tested evolutionary algorithms in terms of the evolution length, measured by the number of fitness function evaluations needed to reach the solution. This evolutionary speedup also translated to an execution speedup whenever the fitness evaluation time was above a small threshold (a fraction of a second). Experiments with LEMd-ISHED on problems of optimizing heat exchangers (evaporators) produced designs that matched or exceeded designs produced by human experts.
dc.description.sponsorshipThis research was conducted in the Machine Learning and Inference Laboratory at George Mason University. The Laboratory's research has been supported in part by the National Science Foundation under Grants No. IIS-0097476 and IIS-9906858, and in part by the UMBC/LUCITE #32 grant.
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dc.identifier.citationCervone, G., Kaufman, K. and Michalski, R. S., "Recent Results from the Experimental Evaluation of the Learnable Evolution Model," Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2002, 2002.
dc.identifier.urihttps://hdl.handle.net/1920/1478
dc.language.isoen_US
dc.relation.ispartofseriesP 02-2
dc.titleRecent Results from the Experimental Evaluation of the Learnable Evolution Model
dc.typeArticle

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