Evolutionary Optimization Techniques on Computational Grids

  • Authors:
  • Baker Abdalhaq;Ana Cortés;Tomàs Margalef;Emilio Luque

  • Affiliations:
  • -;-;-;-

  • Venue:
  • ICCS '02 Proceedings of the International Conference on Computational Science-Part I
  • Year:
  • 2002

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Abstract

Optimization of complex objective functions such as environmental models is a compute-intensive task, difficult to achieve by classical optimization techniques. Evolutionary techniques such as genetic algorithms present themselves as the best alternative to solving this problem. We present a friendly optimization framework for complex objective function on a computational grid platform, which allows easy incorporation of new optimization strategies. This framework was developed using the MW library and the Condor system. The framework architecture is described, and a case study of a forest-fire propagation simulator is then analyzed.