Further experimentations on the scalability of the GEMGA

  • Hillol Kargupta
  • Sanghamitra Bandyopadhyay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1498)


This paper reports the recent developments of the Gene Expression Messy Genetic Algorithm (GEMGA) research. It presents extensive experimental results for large problems with massive multi-modality, non-uniform scaling, and overlapping sub-problems. All the experimental results corroborate the linear time performance of the GEMGA for a wide range of problems, that can be decomposed into smaller overlapping and non-overlapping sub-problems in the chosen representation. These results further support the scalable performance of the GEMGA.


Problem Size Linear Time Performance Fitness Landscape Recombination Operator Extensive Experimental Result 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Hillol Kargupta
    • 1
  • Sanghamitra Bandyopadhyay
    • 2
  1. 1.School of Electrical Engineering and Computer ScienceWashington State UniversityPullmanUSA
  2. 2.Machine Intelligence UnitIndian Statistical InstituteCalcuttaIndia

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