The LEM3 Implementation of Learnable Evolution Model and Its Testing on Complex Function Optimization Problems
Date
2006-07
Authors
Wojtusiak, Janusz
Michalski, Ryszard S.
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Volume Title
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Abstract
Learnable 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.
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.
Keywords
Evolutionary computation, Learnable evolution model, Function optimization, Machine learning
Citation
Wojtusiak, 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.