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2017 | OriginalPaper | Chapter

Adaptive Operator Selection for Many-Objective Optimization with NSGA-III

Authors : Richard A. Gonçalves, Lucas M. Pavelski, Carolina P. de Almeida, Josiel N. Kuk, Sandra M. Venske, Myriam R. Delgado

Published in: Evolutionary Multi-Criterion Optimization

Publisher: Springer International Publishing

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Abstract

The number of objectives in real-world problems has increased in recent years and better algorithms are needed to deal efficiently with it. One possible improvement to such algorithms is the use of adaptive operator selection mechanisms in many-objective optimization algorithms. In this work, two adaptive operator selection mechanisms, Probability Matching (PM) and Adaptive Pursuit (AP), are incorporated into the NSGA-III framework to autonomously select the most suitable operator while solving a many-objective problem. Our proposed approaches, NSGA-III\(_{\text {AP}}\) and NSGA-III\(_{\text {PM}}\), are tested on benchmark instances from the DTLZ and WFG test suits and on instances of the Protein Structure Prediction Problem. Statistical tests are performed to infer the significance of the results. The preliminary results of the proposed approaches are encouraging.

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Footnotes
1
The reference Pareto Front R is a set of N uniformly distributed points on each problem. It can be calculated using a generator code available at https://​github.​com/​JerryI00/​SamplingPF.
 
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Metadata
Title
Adaptive Operator Selection for Many-Objective Optimization with NSGA-III
Authors
Richard A. Gonçalves
Lucas M. Pavelski
Carolina P. de Almeida
Josiel N. Kuk
Sandra M. Venske
Myriam R. Delgado
Copyright Year
2017
DOI
https://doi.org/10.1007/978-3-319-54157-0_19

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