Incentive Method to Handle Constraints in Evolutionary Algorithms with a Case Study

Edward Tsang, Nanlin Jin

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

4 Citations (Scopus)


This paper introduces Incentive Method to handle both hard and soft constraints in an evolutionary algorithm for solving some multi-constraint optimization 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.
Original languageEnglish
Title of host publicationGenetic programming : 9th European conference, EuroGP 2006, Budapest, Hungary, April 10-12, 2006 : proceedings
Place of PublicationLondon
Number of pages360
ISBN (Print)978-3-540-33143-8
Publication statusPublished - 2006


Dive into the research topics of 'Incentive Method to Handle Constraints in Evolutionary Algorithms with a Case Study'. Together they form a unique fingerprint.

Cite this