Cooperative coevolutionary genetic algorithms to find optimal elimination orderings for Bayesian networks

Dong, Xuchu; Yu, Hongfeng; Ouyang, Dantong; Cai, Dianbo; Ye, Yuxin; Zhang, Yonggang

doi:10.1109/bicta.2010.5645605

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…In the last two decades, many CC-based approaches have been applied to increase the performance of metaheuristics for solving real-world LSGO problems (Dong et al, 2010;Maniadakis & Trahanias, 2005). There are three main CC branches depending on variable grouping type: static, random, and learning-based variable grouping.…”

Section: Introductionmentioning

confidence: 99%

Selective Pressure Strategy in Multilevel Cooperative Coevolution for Solving LSGO Problems

Vakhnin¹

2023

European Proceedings of Computers and Technology

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Continuous large-scale global optimization (LSGO) is a challenging task for a wide range of state-of-the art metaheuristic algorithms. This is due to the curse of dimensionality because the size of the search space grows exponentially with the increasing the number of variables. Thus, metaheuristics lose efficiency in solving LSGO problems. For increasing the performance of metaheuristic algorithms in solving LSGO problems, cooperative coevolution (CC) is used. CC-based metaheuristics have two main control parameters, which are the population size and the type of variable grouping. In this paper, a novel selfadaptive multilevel cooperative coevolution algorithm is proposed. The subcomponent optimizer of the proposed CC-based algorithm is SHADE. The proposed algorithm self-adapts the number of subcomponents and the population size during the optimization process. The complete title of the proposed algorithm is CC-SHADE-ML. We have evaluated the performance of the proposed algorithm on fifteen benchmark problems chosen from the LSGO CEC'2013 benchmark set. The performance of the CC-SHADE-ML algorithm has been evaluated using well-known mutation strategies and selection operators. We can conclude that mutation and selection operators make a significant impact in the performance of DE-based metaheuristics. All numerical experiments are proven statistically.

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Section: Introductionmentioning

confidence: 99%

Selective Pressure Strategy in Multilevel Cooperative Coevolution for Solving LSGO Problems

Vakhnin¹

2023

European Proceedings of Computers and Technology

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Fitness function shaping in multiagent cooperative coevolutionary algorithms

Colby

Tumer

2015

Auton Agent Multi-Agent Syst

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Triangulation of Moral Graph Using Bayesian Optimization Algorithm

Zhang¹,

Zhang²,

Jin³

2013

JDCTA

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Finding the optimistic triangulation in Bayesian network, is NP hard. Bayesian Optimization Algorithm is a new kind of evolutionary algorithm-estimation of distribution algorithms (EDAs). An improved BOA is proposed to get approximate optimistic triangulation in this paper. We carry out four EDAs including our method, on four standard Bayesian networks. Comparing with other Estimation of distribution algorithms, our method displays better experimental results and robustness.

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Cooperative coevolutionary genetic algorithms to find optimal elimination orderings for Bayesian networks

Cited by 3 publications

References 23 publications

Selective Pressure Strategy in Multilevel Cooperative Coevolution for Solving LSGO Problems

Selective Pressure Strategy in Multilevel Cooperative Coevolution for Solving LSGO Problems

Fitness function shaping in multiagent cooperative coevolutionary algorithms

Triangulation of Moral Graph Using Bayesian Optimization Algorithm

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