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Search Results (3)

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Keywords = hierarchical coevolutionary algorithm

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6 pages, 278 KB  
Proceeding Paper
Neuro-Evolutionary Synthesis of Game Models of Control under Uncertainty Based on Distributed Computing Technology
by Vladimir A. Serov, Daria L. Popova, Pavel P. Rogalev and Anastasia V. Tararina
Eng. Proc. 2023, 33(1), 59; https://doi.org/10.3390/engproc2023033059 - 25 Jul 2023
Viewed by 1309
Abstract
The methodology basic principles of the neuro-evolutionary synthesis of multi-object multi-criteria systems control models under conflict and uncertainty in real time are discussed. The proposed methodology includes the following main stages: a hierarchical optimization game model under conflict and uncertainty development; a library [...] Read more.
The methodology basic principles of the neuro-evolutionary synthesis of multi-object multi-criteria systems control models under conflict and uncertainty in real time are discussed. The proposed methodology includes the following main stages: a hierarchical optimization game model under conflict and uncertainty development; a library development of hierarchical coevolutionary algorithms for multi-criteria optimization under conflict and uncertainty; software implementation of hierarchical coevolutionary algorithms library based on distributed computing technology; and game algorithms of control under uncertainty synthesis based on the technology of neural networks ensembles. Full article
(This article belongs to the Proceedings of 15th International Conference “Intelligent Systems” (INTELS’22))
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4 pages, 271 KB  
Proceeding Paper
A Hierarchical Model of a Vector Nash Equilibrium Search in a Control Problem under Conflict and Uncertainty
by Vladimir A. Serov and Evgeny M. Voronov
Eng. Proc. 2023, 33(1), 60; https://doi.org/10.3390/engproc2023033060 - 25 Jul 2023
Cited by 1 | Viewed by 993
Abstract
A hierarchical model of a vector Nash equilibrium search under uncertainty is developed. The sufficient conditions for a vector Nash equilibrium of a noncooperative game under uncertainty are formulated, which can be used as a criterion to achieve the required degree of nonquilibrium [...] Read more.
A hierarchical model of a vector Nash equilibrium search under uncertainty is developed. The sufficient conditions for a vector Nash equilibrium of a noncooperative game under uncertainty are formulated, which can be used as a criterion to achieve the required degree of nonquilibrium for an acceptable solution to the problem of multi-object multicriteria systems’ control optimization under conflict and uncertainty. Full article
(This article belongs to the Proceedings of 15th International Conference “Intelligent Systems” (INTELS’22))
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10 pages, 257 KB  
Article
Hierarchical Population Game Models of Coevolution in Multi-Criteria Optimization Problems under Uncertainty
by Vladimir A. Serov
Appl. Sci. 2021, 11(14), 6563; https://doi.org/10.3390/app11146563 - 16 Jul 2021
Cited by 3 | Viewed by 2058
Abstract
The article develops hierarchical population game models of co-evolutionary algorithms for solving the problem of multi-criteria optimization under uncertainty. The principles of vector minimax and vector minimax risk are used as the basic principles of optimality for the problem of multi-criteria optimization under [...] Read more.
The article develops hierarchical population game models of co-evolutionary algorithms for solving the problem of multi-criteria optimization under uncertainty. The principles of vector minimax and vector minimax risk are used as the basic principles of optimality for the problem of multi-criteria optimization under uncertainty. The concept of equilibrium of a hierarchical population game with the right of the first move is defined. The necessary conditions are formulated under which the equilibrium solution of a hierarchical population game is a discrete approximation of the set of optimal solutions to the multi-criteria optimization problem under uncertainty. Full article
(This article belongs to the Special Issue 14th International Conference on Intelligent Systems (INTELS’20))
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