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Approaches for Many-Objective Optimization: Analysis and Comparison on MNK-Landscapes

descriptionPublicationkeyboard_double_arrow_right Part of book or chapter of book , Conference object , Other literature type 01 Jan 2016 English Publisher:Springer International Publishing
Authors: Aguirre, Hernan; Zapotecas-Martínez, Saúl; Liefooghe, Arnaud; Verel, Sébastien; Tanaka, Kiyoshi;

doi: 10.1007/978-3-319-31471-6_2

Approaches for Many-Objective Optimization: Analysis and Comparison on MNK-Landscapes

Abstract

This work analyses the behavior and compares the performance of MOEA/D, IBEA using the binary additive ε and the hypervolume difference indicators, and AεSεH as representative algorithms ofdecomposition, indicators, and ε-dominance based approaches for manyobjective optimization. We use small MNK-landscapes to trace the dynamics of the algorithms generating high-resolution approximations ofthe Pareto optimal set. Also, we use large MNK-landscapes to analyzetheir scalability to larger search spaces.

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Keywords

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Top 10%
Top 10%
Average
Green
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