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)


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.


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.


  1. 1.
    Belding, T.C.: The distributed genetic algorithms revisitedGoogle Scholar
  2. 2.
    Cantú-Paz, E.: Topologies, migration rates, and multi-population parallel genetic algorithmsGoogle Scholar
  3. 3.
    Cantú-Paz, E.: Migration policies, selection pressure, and parallel evolutionary algorithms. IlliGAL Report (99015) (June 1999)Google Scholar
  4. 4.
    Gaioni, R., Davoli, R.: Communication topologies for parallel genetic algorithms: A comparative study on cray t3dGoogle Scholar
  5. 5.
    Grosso, P.: Computer simulations of genetic adaptation: Parallel subcomponent interaction in a multilocus model (1985) Google Scholar
  6. 6.
    Hart, W.: A theoretical comparision of evolutionary algorithms and simulated annealingGoogle Scholar
  7. 7.
  8. 8.
    Sehitoglu, O.T.: Gene reordering and concurrency in genetic algorithms Google Scholar
  9. 9.
    Starkweather, T., Whitley, D., Mathias, K.: Optimization using distributed genetic algorithms. In: Schwefel, H., Maenner, R. (eds.) Parallel Problem Solving from Nature, Berlin, Germany, Springer, Heidelberg (1991)Google Scholar
  10. 10.
    Sunderam, V., Kurzyniec, D.: Lightweight self-organizing frameworks for metacomputing. In: The 11th International Symposium on High Performance Distributed Computing, Edinburgh, Scotland (July 2002)Google Scholar
  11. 11.
    Tanese, R.: Parallel genetic algorithms for a hypercube. In: Proceedings of the Second Conference on Genetic Algorithms (1987)Google Scholar
  12. 12.
    Tanese, R.: Distributed genetic algorithms for function optimalization (1989)Google Scholar
  13. 13.
    Yamaguchi, Y., Maruyama, S.: Amollecular dynamics simulation of the fullerene formation process. Chemical Physics Letters 286, 336–342 (1998)CrossRefGoogle Scholar

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

Personalised recommendations