A New Asynchronous Parallel Evolutionary Algorithm for Function Optimization

  • Pu Liu
  • Francis Lau
  • Michael J. Lewis
  • Cho-li Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)


This paper introduces a new asynchronous parallel evolutionary algorithm (APEA) based on the island model for solving function optimization problems. Our fully distributed APEA overlaps the communication and computation efficiently and is inherently fault-tolerant in a large-scale distributed computing environment. For the scalable BUMP problem, our APEA algorithm achieves the best solution for the 50-dimension problem, and is the first algorithm of which we are aware that can solve the 1,000,000- dimension problem. For other benchmark problems, our APEA finds the best solution to G7 in fewer time steps than [16][17], and finds a better solution to G10 than [17].


Genetic Algorithm Benchmark Problem Markov Chain Model Island Model Parallel Genetic Algorithm 
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 2002

Authors and Affiliations

  • Pu Liu
    • 1
  • Francis Lau
    • 2
  • Michael J. Lewis
    • 1
  • Cho-li Wang
    • 2
  1. 1.Department of Computer ScienceBinghamton University—SUNYBinghamtonUSA
  2. 2.Department of Computer Science and Information SystemsThe University of Hong KongHong Kong

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