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Speeding GA-based attribute selection for image interpretation

  • Communications Session 3A Evolutionary Computation
  • Conference paper
  • First Online:
Foundations of Intelligent Systems (ISMIS 1997)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1325))

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Abstract

This paper addresses the problem of GA-based attribute selection. Previous work in this direction has mainly focused on problem representation so that a genetic algorithm could work on it searching for a satisfactory attribute subset. Even though good experimental results were reported, they were usually acquired at the cost of time. This paper presents a novel approach to this problem. In particular, it introduces attribute quality measure during genetic evolution in order to make some promising attributes more likely to appear in a new generation. In this way, the evolution process is faster, and satisfactory results can be achieved in less time. Preliminary experimental results in image interpretation show that this approach is promising.

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Author information

Authors and Affiliations

  1. Machine Learning and Inference Laboratory, George Mason University, 22030, Fairfax, VA, USA

    Qi Zhang

Authors
  1. Qi Zhang
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Editor information

Zbigniew W. Raś Andrzej Skowron

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© 1997 Springer-Verlag Berlin Heidelberg

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Zhang, Q. (1997). Speeding GA-based attribute selection for image interpretation. In: Raś, Z.W., Skowron, A. (eds) Foundations of Intelligent Systems. ISMIS 1997. Lecture Notes in Computer Science, vol 1325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63614-5_23

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  • DOI: https://doi.org/10.1007/3-540-63614-5_23

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63614-4

  • Online ISBN: 978-3-540-69612-4

  • eBook Packages: Springer Book Archive

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