The Effects of Heterogeneity on Asynchronous Panmictic Genetic Search

  • Boleslaw K. Szymanski
  • Travis Desell
  • Carlos Varela
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4967)


Research scientists increasingly turn to large-scale heterogeneous environments such as computational grids and the Internet based facilities to satisfy their rapidly growing computational needs. The increasing complexity of the scientific models and rapid collection of new data are drastically outpacing the advances in processor speed while the cost of supercomputing environments remains relatively high. However, the heterogeneity and unreliability of these environments, especially the Internet, make scalable and fault tolerant search methods indispensable to effective scientific model verification. An effective search method for these types of environments is asynchronous genetic search, where a population continuously evolves based on asynchronously generated and received results. However, it is unclear what effect heterogeneity has on this type of search. For example, results received from slower workers may turn out to be obsolete or less beneficial than results calculated by faster workers. This paper examines the effect of heterogeneity on asynchronous panmictic (single population) genetic search for two different scientific applications, one used by astronomers to model the Milky Way galaxy and another by particle physicists to determine the existence of theory predicted, yet unobserved particles such as missing baryons. Results show that for both applications results received from slower workers while overall less beneficial are still useful. Additionally, a modification of asynchronous genetic search shows that different parameter generation strategies change their effectiveness over the course of the search.


Genetic Search Parallel Genetic Algorithm Astronomy Application Maximum Population Size Future Generation Computer System 
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 2008

Authors and Affiliations

  • Boleslaw K. Szymanski
    • 1
  • Travis Desell
    • 1
  • Carlos Varela
    • 1
  1. 1.Department of Computer ScienceRensselaer Polytechnic InstituteTroyUSA

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