Incentive Method to Handle Constraints in Evolutionary

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{eurogp06:TsangJin,
  author =       "Edward Tsang and Nanlin Jin",
  title =        "Incentive Method to Handle Constraints in
                 Evolutionary",
  editor =       "Pierre Collet and Marco Tomassini and Marc Ebner and 
                 Steven Gustafson and Anik\'o Ek\'art",
  booktitle =    "Proceedings of the 9th European Conference on Genetic
                 Programming",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3905",
  year =         "2006",
  address =      "Budapest, Hungary",
  month =        "10 - 12 " # apr,
  organisation = "EvoNet",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-33143-3",
  pages =        "133--144",
  DOI =          "doi:10.1007/11729976_12",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  abstract =     "This paper introduces Incentive Method to handle both
                 hard and soft constraints in an evolutionary algorithm
                 for solving some multi-constraint optimisation
                 problems. The Incentive Method uses hard and soft
                 constraints to help allocating heuristic search effort
                 more effectively. The main idea is to modify the
                 objective fitness function by awarding differential
                 incentives according to the defined qualitative
                 preferences, to solution sets which are divided by
                 their satisfaction to constraints. It does not exclude
                 the right to access search spaces that violate some or
                 even all constraints. We test this technique through
                 its application on generating solutions for a classic
                 infinite-horizon extensive-form game. It is solved by
                 an Evolutionary Algorithm incorporated by Incentive
                 method. Experimental results are compared with results
                 from a penalty method and from a non-constraint
                 setting. Statistic analysis suggests that Incentive
                 Method is more effective than the other two techniques
                 for this specific problem.",
  notes =        "Part of \cite{collet:2006:GP} EuroGP'2006 held in
                 conjunction with EvoCOP2006 and EvoWorkshops2006

                 Two Co-evolving populations. Dividing the cake.",
}

Genetic Programming entries for Edward P K Tsang Nanlin Jin

Citations