Incorporating Regular Vines in Estimation of Distribution Algorithms

DOI10.1007/978-3-642-32726-1_2zbMath1251.68216OpenAlexW5441374MaRDI QIDQ4649202

Enrique Villa-Diharce, Rogelio Salinas-Gutiérrez, Arturo Hernández Aguirre

Publication date: 20 November 2012

Published in: EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-642-32726-1_2

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29) Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and heuristics in mathematical programming (90C59) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Cites Work

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• Pair-copula constructions of multiple dependence
• An introduction to copulas.
• Exploitation of linkage learning in evolutionary algorithms
• Schemata, distributions and graphical models in evolutionary optimization
• Vines -- a new graphical model for dependent random variables.
• Probability density decomposition for conditionally dependent random variables modeled by vines
• Modelling of extreme wave heights and periods through copulas
• Matching inductive search bias and problem structure in continuous Estimation-of-Distribution Algorithms
• Scalable optimization via probabilistic modeling. From algorithms to applications.
• Uncertainty Analysis with High Dimensional Dependence Modelling
• Learning Structure Illuminates Black Boxes – An Introduction to Estimation of Distribution Algorithms
• The Shape of Neural Dependence
• Understanding Relationships Using Copulas
• A survey of optimization by building and using probabilistic models

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