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A New Primal-Dual Genetic Algorithm: Case Study for the Winner Determination Problem

  • Madalina Raschip
  • Cornelius Croitoru
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6022)

Abstract

This paper presents a new evolutionary computing strategy which uses the linear programming duality information to help the search for optimum solutions of hard optimization problems. The algorithm is restarted several times when it is stuck into a local optima. At each restart, the appropriate dual space is constructed. A new population of primal individuals is generated using the information from dual solutions and is considered for next iterations. The pursued goal was not to find the best algorithm for solving winner determination problem, but to prove the method’s viability using the problem as a case study. Experiments on realistic instances were performed.

Keywords

Dual Solution Linear Programming Relaxation Combinatorial Auction Stochastic Local Search Winner Determination 
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 2010

Authors and Affiliations

  • Madalina Raschip
    • 1
  • Cornelius Croitoru
    • 1
  1. 1.“Al.I.Cuza” University of IasiRomania

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