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Genetic local search for rule learning

Authors:

Cristiano Grijó Pitangui,

Gerson ZaveruchaAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 1427 - 1428

https://doi.org/10.1145/1389095.1389372

Published: 12 July 2008 Publication History

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Abstract

The performance of Evolutionary Algorithms for combinatorial problems can be significantly improved by adding Local Search, thus obtaining a Genetic Local Search (GLS) also called Memetic Algorithm. In this work, we adapt a previous Stochastic Local Search (SLS) algorithm and embed it into a GBML system. The adapted SLS algorithm works as a module of the system that tries to improve a random individual in the population. We perform experiments to evaluate this adapted SLS procedure and results show that this new GLS system is very effective, not losing in any of the 10 UCI datasets tested when compared to the system without the SLS procedure. The system either obtained significantly more accurate concepts using lower number of rules and features or it achieved the same accuracy as the system without the SLS procedure, but reduced the number of rules and features, and also the time taken to develop the solution.

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Pitangui CZaverucha G(2011)Learning theories using estimation distribution algorithms and (reduced) bottom clausesProceedings of the 21st international conference on Inductive Logic Programming10.1007/978-3-642-31951-8_25(286-301)Online publication date: 31-Jul-2011
https://dl.acm.org/doi/10.1007/978-3-642-31951-8_25

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Genetic local search for rule learning
1. Computing methodologies
  1. Machine learning

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GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

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Published: 12 July 2008

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July 12 - 16, 2008

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Pitangui CZaverucha G(2011)Learning theories using estimation distribution algorithms and (reduced) bottom clausesProceedings of the 21st international conference on Inductive Logic Programming10.1007/978-3-642-31951-8_25(286-301)Online publication date: 31-Jul-2011
https://dl.acm.org/doi/10.1007/978-3-642-31951-8_25

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Genetic algorithm with iterated local search for solving a location-routing problem

Path-relinking intensification methods for stochastic local search algorithms

Adaptive global optimization with local search

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