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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
Piotr Faliszewski, Jakub Sawicki, Robert Schaefer, Maciej Smolka, "Multiwinner Voting in Genetic Algorithms", IEEE Intelligent Systems, vol. 32, no. , pp. 40-48, Jan.-Feb. 2017, doi:10.1109/MIS.2017.5
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