A Genetic Algorithmic Approach to Automated Auction Mechanism Design

Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 271)


In this paper, we present a genetic algorithmic approach to automated auction mechanism design in the context of cat games. This is a follow-up to one piece of our prior work in the domain, the reinforcement learning-based grey-box approach [14]. Our experiments show that given the same search space the grey-box approach is able to produce better auction mechanisms than the genetic algorithmic approach. The comparison can also shed light on the design and evaluation of similar search solutions to other domain problems.


Genetic algorithms Auction mechanism design Double auctions jcat 



Support for this work was provided by PSC-CUNY Award 68800-00 46, jointly funded by the Professional Staff Congress and the City University of New York. The authors acknowledge resources from the computational facility at the CUNY High Performance Computing Center, which is operated by the College of Staten Island and funded, in part, by grants from the City of New York, State of New York, CUNY Research Foundation, and National Science Foundation Grants CNS-0958379, CNS-0855217 and ACI 1126113.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Guttman Community CollegeCity University of New YorkNew YorkUSA
  2. 2.Department of InformaticsKing’s College LondonLondonUK

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