Abstract
The scale-up performance of genetic algorithms applied to group decision making problems is investigated. Ordinal intervals are used for expressing the individual preferences of the decision makers, as provided independently for each course of action. Genetic algorithms have been found capable of swiftly returning optimal ranking solutions, with computational complexity (the relationship between the number of available courses of action and the number of generations until convergence) expressed by a fourth order polynomial, but found practically independent of the number of decision makers.
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References
Moore, C.M.: Group Techniques for Idea Building (Applied Social Research Methods). Sage Publications Inc., Thousand Oaks (1987)
Hwang, C.L., Lin, M.J.: Group Decision Making under Multiple Criteria: Methods and Applications. Springer, Berlin (1987)
Goldberg, D.E.: Genetic Algorithms in Search Optimization and Machine Learning. Addison Wesley (1989)
Borda, J.C.: Memoire sur les Elections an Scrutin, Histoire de l’ Academie Royale de Science, Paris (1784)
Kendall, M.: Rank Correction Methods, 3rd edn. Hafners, New York (1962)
Jakobs, S.: On genetic algorithms for the packing of polygons. European Journal of Operational Research 88, 165–181 (1996)
Islei, G., Lockett, G.: Group decision making: suppositions and practice. Socio-Economic Planning Sciences 25, 67–82 (1991)
Wang, Y.M., Yang, J.B., Xu, D.L.: A preference aggregation method through the estimation of utility intervals. Computers & Operations Research 32, 2027–2049 (2005)
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Tambouratzis, T., Kanellidis, V. (2013). The Scale-Up Performance of Genetic Algorithms Applied to Group Decision Making Problems. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2013. Lecture Notes in Computer Science, vol 7824. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37213-1_17
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DOI: https://doi.org/10.1007/978-3-642-37213-1_17
Publisher Name: Springer, Berlin, Heidelberg
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