- 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.",
