Speeding up many-objective optimization by Monte Carlo approximations

Many state-of-the-art evolutionary vector optimization algorithms compute the contributing hypervolume for ranking candidate solutions. However, with an increasing number of objectives, calculating the volumes becomes intractable. Therefore, although hypervolume-based algorithms are often the method...

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Bibliographic Details
Published in:	Artificial intelligence Vol. 204; pp. 22 - 29
Main Authors:	Bringmann, Karl, Friedrich, Tobias, Igel, Christian, Voß, Thomas
Format:	Journal Article
Language:	English
Published:	Oxford Elsevier B.V 01.11.2013 Elsevier
Subjects:	Algorithmics. Computability. Computer arithmetics Applied sciences Computer science; control theory; systems Decision theory. Utility theory Evolutionary algorithm Exact sciences and technology Hypervolume indicator Mathematical programming Multi-objective optimization Operational research and scientific management Operational research. Management science Pareto-front approximation Theoretical computing Pareto-front approximation Multi-objective optimization Hypervolume indicator Evolutionary algorithm Monte Carlo method Dominating set Pareto optimum Hierarchical classification Objective analysis Multiobjective programming Covariance matrix Approximation algorithm Vector method Modeling Function evaluation Computation time Budget Objective function Vector optimization
ISSN:	0004-3702, 1872-7921
Online Access:	Get full text
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