Speeding GA-based attribute selection for image interpretation

  • Qi Zhang
Communications Session 3A Evolutionary Computation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1325)


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.


evolutionary computation attribute selection image interpretation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Qi Zhang
    • 1
  1. 1.Machine Learning and Inference LaboratoryGeorge Mason UniversityFairfaxUSA

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