Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Beligiannis, G., Tsirogiannis, G.A., Pintelas, P.E.: Restarting: a technique to improve classic genetic algorithms’ performance. In: Proceedings of World Academy of Science, Engineering and Technology, vol. 1, pp. 144–147 (2005)
Chen, Y., Hu, J., Hirasawa, K., Yu, S.: Performance tuning of genetic algorithms with reserve selection. In: IEEE Congress on Evolutionary Computation, pp. 2202–2209 (2007)
Cobb, H.G.: Genetics algorithms for tracking changing environments. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 523–530. Morgan Kaufmann (1993)
Combarro, E.F., Miranda, P.: Identifying of fuzzy measures from sample data with genetic algorithms. Computational Operational Reseach 33(10), 3046–3066 (2006)
Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)
De Jong, K.A.: An Analysis of the behavior of a class of genetic adaptive systems. PhD thesis (1975)
Denneberg, D., Grabisch, M.: Shapley value and interaction index. Mathematics of interaction index (1996)
Diggle, P.J.: Statistical analysis of spatial point patterns. Academic Press, London (1983)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional (1980)
Grabisch, M.: The interaction and Mobius representation of fuzzy measures on finite spaces, k-additive measures: a survey. In: Grabisch, M., Murofushi, T., Sugeno, M. (eds.) Fuzzy Measures and Integrals: Theory and Applications. Physica Verlag (2000)
Grabisch, M., Roubens, M.: Application of the Choquet integral in multicriteria decision making. In: Garbisch, M., Murofushi, T., Sugeno, M. (eds.) Fuzzy Measures and Integrals: Theory and Applications. Physica Verlag (2000)
Grefenstette, J.J.: Genetic algorithms for changing environments. In: Parallel Problem Solving from Nature 2, pp. 137–144. Elsevier (1992)
Holland, J.H.: Adaption in natural artificial systems. University of Michigan Press, Ann Arbor (1975)
Karci, A.: Novelty in the generation of initial population for genetic algorithms. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES 2004. LNCS (LNAI), vol. 3214, pp. 268–275. Springer, Heidelberg (2004)
Kreinovich, V., Quintana, C., Fuentes, O.: Genetic algorithms: what fitness scaling is optimal. Cybernetics and Systems: an International Journal 24, 9–26 (1993)
Maaranen, H., Miettinen, K., Penttinen, A.: On initial populations of a genetic algorithm for continuous optimization problems. Journal of Global Optimization 37(3), 405–436 (2007)
McKay, M.D., Beckman, R.J., Conover, W.J.: A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21(2), 239–245 (1979)
Niederreither, H.: Random Number Generation and Quasi-Monte Carlo Methods. SIAM, Philadelphia (1992)
Ripley, B.D.: Spatial statistics. John Wiley & Sons, New York (1981)
Shapley, L.S.: A value for n-person games. In: Kuhn, H.W., Tucker, A.W. (eds.) Contributions to the Theory of Games, vol. 2, pp. 307–317. Princeton University Press (1953)
Tsutsui, S., Fujimoto, Y., Ghosh, A.: Forking genetic algorithms: GAs with search space division schemes. Evolutionary Computation 5(1), 61–80 (1997)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Magoč, T., Modave, F. (2014). Optimization of the Choquet Integral Using Genetic Algorithm. In: Ceberio, M., Kreinovich, V. (eds) Constraint Programming and Decision Making. Studies in Computational Intelligence, vol 539. Springer, Cham. https://doi.org/10.1007/978-3-319-04280-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-04280-0_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04279-4
Online ISBN: 978-3-319-04280-0
eBook Packages: EngineeringEngineering (R0)