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Application to Winner Determination Problems in Combinatorial Auctions

  • Jingqiao Zhang
  • Arthur C. Sanderson
Chapter
  • 1.1k Downloads
Part of the Adaptation Learning and Optimization book series (ALO, volume 1)

Abstract

The crucial idea behind DE is the scheme of generating mutant vectors using the weighted difference of other vectors randomly chosen from the population. This mutation operation relies on arithmetic operations (add, subtract, multiply, etc.) rather than general data manipulation operations (sort, swap, permute, etc.). It cannot be directly extended to the discrete combinatorial space.

In this chapter, we apply parameter adaptive differential evolution to a seminal combinatorial optimization problem of winner determination in Combinatorial Auctions (CAs). To adapt JADE to this problem, we use a rank-based representation scheme that produces only feasible solutions and a regeneration operation that constricts the search space. It is shown that JADE compares favorably to the classic DE algorithm, a local stochastic search algorithm Casanova, and a genetic algorithm based approach SGA.

Keywords

Differential Evolution Differential Evolution Algorithm Combinatorial Auction Regeneration Operation Equivalent Solution 
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 2009

Authors and Affiliations

  • Jingqiao Zhang
    • Arthur C. Sanderson

      There are no affiliations available

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