Analysis of Speedups in Parallel Evolutionary Algorithms for Combinatorial Optimization

(Extended Abstract)
  • Jörg Lässig
  • Dirk Sudholt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7074)


Evolutionary algorithms are popular heuristics for solving various combinatorial problems as they are easy to apply and often produce good results. Island models parallelize evolution by using different populations, called islands, which are connected by a graph structure as communication topology. Each island periodically communicates copies of good solutions to neighboring islands in a process called migration. We consider the speedup gained by island models in terms of the parallel running time for problems from combinatorial optimization: sorting (as maximization of sortedness), shortest paths, and Eulerian cycles. Different search operators are considered. The results show in which settings and up to what degree evolutionary algorithms can be parallelized efficiently. Along the way, we also investigate how island models deal with plateaus. In particular, we show that natural settings lead to exponential vs. logarithmic speedups, depending on the frequency of migration.


Evolutionary Algorithm Mutation Operator Linear Speedup Short Path Problem Search Point 
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 2011

Authors and Affiliations

  • Jörg Lässig
    • 1
  • Dirk Sudholt
    • 2
  1. 1.EAD GroupUniversity of Applied Sciences Zittau/GörlitzGörlitzGermany
  2. 2.CERCIAUniversity of BirminghamBirminghamUK

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