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Evolutionary large-scale multi-objective optimization and applications = integrating evolutionary computation, machine learning, and data science /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Evolutionary large-scale multi-objective optimization and applications/ Xingyi Zhang ... [et al.]
Reminder of title:	integrating evolutionary computation, machine learning, and data science /
other author:	Zhang, Xingyi.
Published:	Piscataway, NJ :IEEE ; : c2024.,
Description:	1 online resource (xxiv, 328 p.) :ill. (some col.) :
Subject:	Mathematical optimization. -
Online resource:	https://onlinelibrary.wiley.com/doi/book/10.1002/9781394178445
ISBN:	9781394178445

Evolutionary large-scale multi-objective optimization and applications = integrating evolutionary computation, machine learning, and data science /
Evolutionary large-scale multi-objective optimization and applicationsintegrating evolutionary computation, machine learning, and data science /[electronic resource] :Xingyi Zhang ... [et al.] - 1st ed. - Piscataway, NJ :IEEE ;c2024. - 1 online resource (xxiv, 328 p.) :ill. (some col.)

Includes bibliographical references and index.

Multi-objective evolutionary algorithms and evolutionary large-scale optimization -- Evolutionary large-scale multi-objective optimization -- Evolutionary algorithms for large-scale multi-objective optimization -- Evolutionary algorithms for sparse large-scale multi-objective optimization -- Evolutionary large-scale multi-objective optimization for community detection in complex networks -- Evolutionary large-scale multi-objective optimization in logistics scheduling -- Evolutionary large-scale multi-objective optimization in power systems -- Evolutionary large-scale multi-objective optimization in radiotherapy planning -- Evolutionary large-scale multi-objective optimization in deep learning.

"Multi-objective optimization problems (MOPs) widely exist in scientific research and engineering designs. Evolutionary algorithms (EAs) have shown promising potential in solving various MOPs. However, their performance may deteriorate drastically when tackling problems involving a large number of decision variables, i.e., the large-scale multi-objective optimization problems (LSMOPs). In recent years, increasing efforts have been devoted to addressing the challenges brought by such LSMOPs."--

ISBN: 9781394178445Subjects--Topical Terms:

527675
Mathematical optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Evolutionary large-scale multi-objective optimization and applications = integrating evolutionary computation, machine learning, and data science /
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245 0 0 $a Evolutionary large-scale multi-objective optimization and applications $h [electronic resource] : $b integrating evolutionary computation, machine learning, and data science / $c Xingyi Zhang ... [et al.]
250 $a 1st ed.
260 $a Piscataway, NJ : $a Hoboken, NJ : $b IEEE ; $c c2024. $b John Wiley & Sons,
300 $a 1 online resource (xxiv, 328 p.) : $b ill. (some col.)
504 $a Includes bibliographical references and index.
505 0 $a Multi-objective evolutionary algorithms and evolutionary large-scale optimization -- Evolutionary large-scale multi-objective optimization -- Evolutionary algorithms for large-scale multi-objective optimization -- Evolutionary algorithms for sparse large-scale multi-objective optimization -- Evolutionary large-scale multi-objective optimization for community detection in complex networks -- Evolutionary large-scale multi-objective optimization in logistics scheduling -- Evolutionary large-scale multi-objective optimization in power systems -- Evolutionary large-scale multi-objective optimization in radiotherapy planning -- Evolutionary large-scale multi-objective optimization in deep learning.
520 $a "Multi-objective optimization problems (MOPs) widely exist in scientific research and engineering designs. Evolutionary algorithms (EAs) have shown promising potential in solving various MOPs. However, their performance may deteriorate drastically when tackling problems involving a large number of decision variables, i.e., the large-scale multi-objective optimization problems (LSMOPs). In recent years, increasing efforts have been devoted to addressing the challenges brought by such LSMOPs."-- $c Provided by publisher.
588 $a Description based on online resource; title from digital title page (viewed on July 24, 2024)
650 0 $a Mathematical optimization. $3 527675
650 0 $a Evolutionary computation. $3 573187
650 0 $a Machine learning. $3 561253
700 1 $a Zhang, Xingyi. $e editor. $3 1265126
856 4 0 $u https://onlinelibrary.wiley.com/doi/book/10.1002/9781394178445