The Development of the Inductive Database System VINLEN: A Review of Current Research

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dc.contributor.authorCervone, Guido
dc.contributor.authorKaufman, Kenneth A.
dc.contributor.authorMichalski, Ryszard S.
dc.date.accessioned2006-11-03T18:17:22Z
dc.date.available2006-11-03T18:17:22Z
dc.date.issued2003-06
dc.description.abstractThe recently introduced 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. Initial experiments with a preliminary implementation of LEM were highly encouraging, but tentative. This paper presents results from a new study 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 ISHED, an implementation oriented toward engineering design. In all cases of function optimization, LEM2 strongly outperformed tested evolutionary algorithms in terms of the evolution length, measured by the number of fitness function evaluations needed to reach the desired 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). The most important result of the study was that the advantage of LEM2 over the tested Darwinian-style evolutionary methods in terms of evolution length grew rapidly with the growth of the complexity of the optimized function. Experiments with ISHED on problems of optimizing heat exchangers (evaporators) produced designs that matched or exceeded designs produced by human experts. The obtained very strong results from the application of the LEM methodology to two diverse domains suggest that it may be useful also in other application domains, especially, those in which the fitness function evaluation is time-consuming or complex.
dc.description.sponsorshipThis research has been 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., "Validating Learnable Evolution Model on Selected Optimization and Design Problems," Reports of the Machine Learning and Inference Laboratory, MLI 03-1, George Mason University, Fairfax, VA, June, 2003.
dc.identifier.urihttps://hdl.handle.net/1920/1482
dc.language.isoen_US
dc.relation.ispartofseriesP 03-2
dc.relation.ispartofseriesMLI 03-1
dc.subjectMachine learning
dc.subjectEvolutionary computation
dc.subjectFunction optimization
dc.subjectLearnable evolution model
dc.subjectEngineering design
dc.subjectMultistrategy learning
dc.titleThe Development of the Inductive Database System VINLEN: A Review of Current Research
dc.typeTechnical report

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