Abstract
Genetic algorithms are a group of powerful tools for solving ill-posed global optimization problems in continuous domains. In case in which the insensitivity of the fitness function is the main obstacle, the most desired feature of a genetic algorithm is its ability to explore plateaus of the fitness function, surrounding its minimizers. In this paper we suggest a way of maintaining diversity of the population in the plateau regions, based on a new approach for the selection based on the theory of multiwinner elections among autonomous agents. The paper delivers a detailed description of the new selection algorithm, computational experiments that guide 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.
The work presented in this paper has been partially supported by Polish NCN grant no. DEC-2015/17/B/ST6/01867 and by the AGH grant no. 11.11.230.124.
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Notes
- 1.
From the point of view of the elections theory, our setting is an example of two-dimensional Euclidean single-peaked preferences. Under two-dimensional Euclidean preferences, every voter and every candidate is a point in a two-dimensional Euclidean space and every voter (in our case, every individual) derives his or her preference orders by sorting the candidates (in our case, the individuals) with respect to their Euclidean distance from him or herself.
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Faliszewski, P., Sawicki, J., Schaefer, R., Smołka, M. (2016). Multiwinner Voting in Genetic Algorithms for Solving Ill-Posed Global Optimization Problems. In: Squillero, G., Burelli, P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science(), vol 9597. Springer, Cham. https://doi.org/10.1007/978-3-319-31204-0_27
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