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

  • Edward Tsang
  • Nanlin Jin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3905)


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.


Evolutionary Algorithm Genetic Program Penalty Method Soft Constraint Hard Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Edward Tsang
    • 1
  • Nanlin Jin
    • 1
  1. 1.Department of Computer ScienceUniversity of EssexColchesterUnited Kingdom

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