Various Degrees of Steadiness in NSGA-II and Their Influence on the Quality of Results

Maxim Buzdalov; Vladimir Parfenov

doi:10.1145/2739482.2764887

Various Degrees of Steadiness in NSGA-II and Their Influence on the Quality of Results

Maxim Buzdalov, Vladimir Parfenov

ITMO University

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

5 Citations (Scopus)

Abstract

Steady-state evolutionary algorithms are often favoured over generational ones due to better scalability in parallel and distributed environments. However, in certain conditions they are able to produce results of better quality as well. We consider several ways to introduce various ``degrees of steadiness'' in the NSGA-II algorithm, some of which have not been known in literature, and show experimentally (on a corpus of 21 test problems) the presence of a general trend: algorithms with more steadiness yield better results.

Original language	English
Title of host publication	GECCO Companion '15
Subtitle of host publication	Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation
Editors	Sara Silva
Publisher	Association for Computing Machinery
Pages	749-750
Number of pages	2
ISBN (Print)	978-1-4503-3488-4
DOIs	https://doi.org/10.1145/2739482.2764887
Publication status	Published - 11 Jul 2015
Externally published	Yes
Event	16th Genetic and Evolutionary Computation Conference, GECCO 2015 - Madrid, Spain Duration: 11 Jul 2015 → 15 Jul 2015

Conference

Conference	16th Genetic and Evolutionary Computation Conference, GECCO 2015
Country/Territory	Spain
City	Madrid
Period	11 Jul 2015 → 15 Jul 2015

Keywords

multi-objective
nsga-ii
steady-state

Access to Document

10.1145/2739482.2764887

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title = "Various Degrees of Steadiness in NSGA-II and Their Influence on the Quality of Results",

abstract = "Steady-state evolutionary algorithms are often favoured over generational ones due to better scalability in parallel and distributed environments. However, in certain conditions they are able to produce results of better quality as well. We consider several ways to introduce various ``degrees of steadiness'' in the NSGA-II algorithm, some of which have not been known in literature, and show experimentally (on a corpus of 21 test problems) the presence of a general trend: algorithms with more steadiness yield better results.",

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Buzdalov, M & Parfenov, V 2015, Various Degrees of Steadiness in NSGA-II and Their Influence on the Quality of Results. in S Silva (ed.), GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation. Association for Computing Machinery, pp. 749-750, 16th Genetic and Evolutionary Computation Conference, GECCO 2015, Madrid, Spain, 11 Jul 2015. https://doi.org/10.1145/2739482.2764887

Various Degrees of Steadiness in NSGA-II and Their Influence on the Quality of Results. / Buzdalov, Maxim; Parfenov, Vladimir.
GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation. ed. / Sara Silva. Association for Computing Machinery, 2015. p. 749-750.

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

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N2 - Steady-state evolutionary algorithms are often favoured over generational ones due to better scalability in parallel and distributed environments. However, in certain conditions they are able to produce results of better quality as well. We consider several ways to introduce various ``degrees of steadiness'' in the NSGA-II algorithm, some of which have not been known in literature, and show experimentally (on a corpus of 21 test problems) the presence of a general trend: algorithms with more steadiness yield better results.

AB - Steady-state evolutionary algorithms are often favoured over generational ones due to better scalability in parallel and distributed environments. However, in certain conditions they are able to produce results of better quality as well. We consider several ways to introduce various ``degrees of steadiness'' in the NSGA-II algorithm, some of which have not been known in literature, and show experimentally (on a corpus of 21 test problems) the presence of a general trend: algorithms with more steadiness yield better results.

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Various Degrees of Steadiness in NSGA-II and Their Influence on the Quality of Results

Abstract

Conference

Keywords

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