A hybrid model-classifier framework for managing prediction uncertainty in expensive optimisation problems

DOI10.1080/00207721.2011.602482zbMath1307.90208OpenAlexW2029385462MaRDI QIDQ5497417

Kazuhiro Izui, Yoel Tenne, Shinji Nishiwaki

Publication date: 4 February 2015

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207721.2011.602482

zbMATH Keywords

classificationmodellingevolutionary computationuncertainityknowledge based systemsbiologically inspired algorithmsexpensive optimization problems

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Control/observation systems with incomplete information (93C41)

Related Items (3)

On the Ball-Constrained Weighted Maximin Dispersion Problem ⋮ Hybrid real-code ant colony optimisation for constrained mechanical design ⋮ A hybrid model-classifier framework for managing prediction uncertainty in expensive optimisation problems

Uses Software

• GAToolBox
• XFOIL

Cites Work

• Unnamed Item
• Computational intelligence in expensive optimization problems
• Miscellaneous error bounds for multiquadric and related interpolators
• Efficient global optimization of expensive black-box functions
• Hybridization of a multi-objective genetic algorithm, a neural network and a classical optimizer for a complex design problem in fluid dynamics
• Optimal global rates of convergence for nonparametric regression
• Surrogate assisted local search in PMSM drive design
• Trust Region Methods
• Comparison of Model Selection for Regression
• A hybrid model-classifier framework for managing prediction uncertainty in expensive optimisation problems

This page was built for publication: A hybrid model-classifier framework for managing prediction uncertainty in expensive optimisation problems