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Issue No. 01 - Jan.-Feb. (2017 vol. 32)
ISSN: 1541-1672
pp: 40-48
Piotr Faliszewski , AGH University of Science and Technology
Jakub Sawicki , AGH University of Science and Technology
Robert Schaefer , AGH University of Science and Technology
Maciej Smolka , AGH University of Science and Technology
Genetic algorithms are a group of powerful tools for solving ill-posed global optimization problems in continuous domains. When insensitivity in the fitness function is an obstacle, the most desired feature of a genetic algorithm is its ability to explore plateaus of the fitness function surrounding its minimizers. The authors suggest a way of maintaining diversity of the population in the plateau regions based on a new approach for selection according to the theory of multiwinner elections among autonomous agents. The article delivers a detailed description of the new selection algorithm, computational experiments that put the choice of the proper multiwinner rule to use, and a preliminary experiment showing the proposed algorithm's effectiveness in exploring a fitness function's plateau.
Social factors, Statistics, Artificial intelligence, Economics, Genetic algorithms

P. Faliszewski, J. Sawicki, R. Schaefer and M. Smolka, "Multiwinner Voting in Genetic Algorithms," in IEEE Intelligent Systems, vol. 32, no. 1, pp. 40-48, 2017.
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