Evolving optimal agendas for package deal negotiation

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@InProceedings{Fatima:2011:GECCO,
  author =       "Shaheen Fatima and Ahmed Kattan",
  title =        "Evolving optimal agendas for package deal
                 negotiation",
  booktitle =    "GECCO '11: Proceedings of the 13th annual conference
                 on Genetic and evolutionary computation",
  year =         "2011",
  editor =       "Natalio Krasnogor and Pier Luca Lanzi and 
                 Andries Engelbrecht and David Pelta and Carlos Gershenson and 
                 Giovanni Squillero and Alex Freitas and 
                 Marylyn Ritchie and Mike Preuss and Christian Gagne and 
                 Yew Soon Ong and Guenther Raidl and Marcus Gallager and 
                 Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and 
                 Nikolaus Hansen and Silja Meyer-Nieberg and 
                 Jim Smith and Gus Eiben and Ester Bernado-Mansilla and 
                 Will Browne and Lee Spector and Tina Yu and Jeff Clune and 
                 Greg Hornby and Man-Leung Wong and Pierre Collet and 
                 Steve Gustafson and Jean-Paul Watson and 
                 Moshe Sipper and Simon Poulding and Gabriela Ochoa and 
                 Marc Schoenauer and Carsten Witt and Anne Auger",
  isbn13 =       "978-1-4503-0557-0",
  pages =        "505--512",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 combinatorial optimization and metaheuristics",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001576.2001646",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "This paper presents a hyper GA system to evolve
                 optimal agendas for package deal negotiation. The
                 proposed system uses a Surrogate Model based on Radial
                 Basis Function Networks (RBFNs) to speed up the
                 evolution. The negotiation scenario is as follows.
                 There are two negotiators/agents (a and b) and m
                 issues/items available for negotiation. But from these
                 m issues, the agents must choose g issues and negotiate
                 on them. The g issues thus chosen form the agenda. The
                 agenda is important because the outcome of negotiation
                 depends on it. Furthermore, a and b will, in general,
                 get different utilities/profits from different agendas.
                 Thus, for competitive negotiation (i.e., negotiation
                 where each agent wants to maximise its own utility),
                 each agent wants to choose an agenda that maximizes its
                 own profit. However, the problem of determining an
                 agent's optimal agenda is complex, as it requires
                 combinatorial search. To overcome this problem, we
                 present a hyper GA method that uses a Surrogate Model
                 based on Radial Basis Function Networks (RBFNs) to find
                 an agent's optimal agenda. The performance of the
                 proposed method is evaluated experimentally. The
                 results of these experiments demonstrate that the
                 surrogate assisted algorithm, on average, performs
                 better than standard GA and random search.",
  notes =        "Also known as \cite{2001646} GECCO-2011 A joint
                 meeting of the twentieth international conference on
                 genetic algorithms (ICGA-2011) and the sixteenth annual
                 genetic programming conference (GP-2011)",
}

Genetic Programming entries for Shaheen Fatima Ahmed Kattan

Citations