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The Genetic Algorithms Population Pluglet for the H2O Metacomputing System

  • Tomasz Ampuła
  • Dawid Kurzyniec
  • Vaidy Sunderam
  • Henryk Witek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3036)

Abstract

This paper describes GAPP – a framework for the execution of distributed genetic algorithms (GAs) using the H2O metacomputing environment. GAs may be a viable solution technique to intractable problems; GAPP offers a distributed GA framework that can lead to rapid and efficient parallel execution of GAs from a variety of domains, with very little effort on behalf of the application scientist. It is premised upon the common phases embodied in GA lifecycles and contains modular implementations to handle each of them, whereas end applications simply provide domain-specific functions and parameters. GAPP is built for H2O, a component-oriented metacomputing system that enables cooperative resource sharing and flexible, reconfigurable concurrent computing on heterogeneous platforms. Experiences with the use of GAPP on H2O are described and preliminary results are very encouraging.

Keywords

Genetic Algorithm Island Model Tour Length Parallel Genetic Algorithm Genetic Algorithm Population 
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 2004

Authors and Affiliations

  • Tomasz Ampuła
    • 1
  • Dawid Kurzyniec
    • 1
  • Vaidy Sunderam
    • 1
  • Henryk Witek
    • 2
  1. 1.Dept. of Math and Computer ScienceEmory UniversityAtlantaUSA
  2. 2.Cherry L. Emerson Center of Scientific Computation and Dept. of ChemistryEmory UniversityAtlantaUSA

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