A study on many-objective optimization using the Kriging-surrogate-based evolutionary algorithm maximizing expected hypervolume improvement

DOI10.1155/2015/162712zbMath1394.90517OpenAlexW1932726085WikidataQ59117435 ScholiaQ59117435MaRDI QIDQ1664820

Koji Shimoyama, Shigeru Obayashi, Chang Luo

Publication date: 27 August 2018

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2015/162712

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29) Stochastic programming (90C15)

Uses Software

• EGO
• Borg
• HypE

Cites Work

• Efficient global optimization of expensive black-box functions
• A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
• Evolutionary Algorithms for Solving Multi-Objective Problems
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