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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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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