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Artificial Intelligence - Special volume on empirical methods
LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning
Machine Learning - Special issue on multistrategy learning
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PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
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ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary Optimization Guided by Entropy-Based Discretization
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Closed-loop evolutionary multiobjective optimization
IEEE Computational Intelligence Magazine
An integrated multi-task inductive database VINLEN: initial implementation and early results
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
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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 type 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 evolution length (the number of fitness evaluations needed to achieved a desired solution), sometimes more than by one order of magnitude.