Optimization of the Choquet Integral Using Genetic Algorithm

  • Tanja MagočEmail author
  • François Modave
Part of the Studies in Computational Intelligence book series (SCI, volume 539)


Decision making in an unavoidable part of out daily lives. Many decisions are straightforward, but others require careful consideration of each alternative and many attributes characterizing each alternative. If these attributes are mutually dependent, the Choquet integral is a technique often used for modeling the decision making problem. With a large number of attributes to consider, decision making becomes an optimization problem that requires huge computational resources in order to be solved exactly. Instead of using a large amount of these resources, heuristic techniques have been used to speed the computations and find a suboptimal decision. Yet, these heuristic methods could be improved to find better approximation with minimal increase in required computational resources. Genetic algorithm has been used in many situations as a heuristic optimization technique. In this paper, we present some modifications to the genetic algorithm that allow more precise optimization.


Genetic Algorithm Search Space Initial Population Fuzzy Measure Global Preference 
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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© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Texas at El PasoEl PasoUSA

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