Genetic algorithms for feature weighting: evolution vs. coevolution and darwin vs. lamarck

Authors:
Alexandre Blansché;Pierre Gançarski;Jerzy J. Korczak
Affiliations:
LSIIT, UMR 7005 CNRS-ULP, Parc d'Innovation, Illkirch, France;LSIIT, UMR 7005 CNRS-ULP, Parc d'Innovation, Illkirch, France;LSIIT, UMR 7005 CNRS-ULP, Parc d'Innovation, Illkirch, France
Venue:
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Year:
2005

Citing 8
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Weighting Unusual Feature Types

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Comparison between two coevolutionary feature weighting algorithms in clustering

Pattern Recognition

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Abstract

Feature weighting is a more and more important step in clustering because data become more and more complex. An embedded local feature weighting method has been proposed in [1]. In this paper, we present a new method based on the same cost function, but performed through a genetic algorithm. The learning process can be performed through an evolutionary approach or through a cooperavive coevolutionary approach. Moreover, the genetic algorithm can be combined with the original Weighting K-means algorithm in a Lamarckian learning paradigm. We compare hill-climbing optimization versus genetic algorithms, evolutionary versus coevolutionary approaches, and Darwinian versus Lamarckian learning on different datasets. The results seem to show that, on the datasets where the original algorithm is efficient, the proposed methods are even better.