A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling

To approximate the Pareto front, most existing multiobjective evolutionary algorithms store the nondominated solutions found so far in the population or in an external archive during the search. Such algorithms often require a high degree of diversity of the stored solutions and only a limited numbe...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on evolutionary computation Vol. 19; no. 6; pp. 838 - 856
Main Authors:	Ran Cheng, Yaochu Jin, Narukawa, Kaname, Sendhoff, Bernhard
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.12.2015 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms estimation of distribution algorithms Gaussian processes inverse modeling Inverse problems Job shops Multiobjective optimization Pareto optimization random grouping Sociology Stochastic models Vectors inverse modeling multiobjective optimization (MOO) random grouping Gaussian processes (GPs) Estimation of distribution algorithms (EDAs)
ISSN:	1089-778X, 1941-0026
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!