The LEM3 Implementation of Learnable Evolution Model and Its Testing on Complex Function Optimization Problems

Simple item page
dc.contributor.authorWojtusiak, Janusz
dc.contributor.authorMichalski, Ryszard S.
dc.date.accessioned2006-11-03T18:17:38Z
dc.date.available2006-11-03T18:17:38Z
dc.date.issued2006-07
dc.description© ACM, 2006. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution.
dc.description.abstractLearnable Evolution Model (LEM) is a form of non-Darwinian evolutionary computation that employs machine learning to guide evolutionary processes. Its main novelty are new types of operators for creating new individuals, specifically, hypothesis generation, which learns rules indicating subareas in the search space that likely contain the optimum, and hypothesis instantiation, which populates these subspaces with new individuals. This paper briefly describes the newest and most advanced implementation of learnable evolution, LEM3, its novel features, and results from its comparison with a conventional, Darwinian-type evolutionary computation program (EA), a cultural evolution algorithm (CA), and the estimation of distribution algorithm (EDA) on selected function optimization problems (with the number of variables varying up to 1000). In every experiment, LEM3 outperformed the compared programs in terms of the evaluation length (the number of fitness evaluations needed to achieve a desired solution), sometimes more than by one order of magnitude.
dc.format.extent2641 bytes
dc.format.extent119634 bytes
dc.format.mimetypetext/xml
dc.format.mimetypeapplication/pdf
dc.identifier.citationWojtusiak, J. and Michalski, R. S., "The LEM3 Implementation of Learnable Evolution Model and Its Testing on Complex Function Optimization Problems," Proceedings of Genetic and Evolutionary Computation Conference, GECCO 2006, Seattle, WA, July 8-12, 2006.
dc.identifier.urihttps://hdl.handle.net/1920/1501
dc.language.isoen_US
dc.relation.ispartofseriesP 06-7
dc.subjectEvolutionary computation
dc.subjectLearnable evolution model
dc.subjectFunction optimization
dc.subjectMachine learning
dc.titleThe LEM3 Implementation of Learnable Evolution Model and Its Testing on Complex Function Optimization Problems
dc.typePresentation

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
06-07.pdf
Size:
116.83 KB
Format:
Adobe Portable Document Format
Download

Collections

Machine Learning and Inference Laboratory, College of Public Health