東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Machine learning assisted evolutionary multi- and many- objective optimization

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning assisted evolutionary multi- and many- objective optimization/ by Dhish Kumar Saxena ... [et al.].
other author:	Saxena, Dhish Kumar.
Published:	Singapore :Springer Nature Singapore : : 2024.,
Description:	xv, 244 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Introduction -- Optimization Problems and Algorithms -- Existing Machine Learning Studies on Multi-objective Optimization -- Learning to Converge Better and Faster -- Learning to Diversify Better and Faster -- Learning to Simultaneously Converge and Diversify Better and Faster -- Learning to Understand the Problem Structure -- ML-Assisted Analysis of Pareto-optimal Front -- Further Machine Learning Assisted Enhancements -- Conclusions.
Contained By:	Springer Nature eBook
Subject:	Mathematical optimization. -
Online resource:	https://doi.org/10.1007/978-981-99-2096-9
ISBN:	9789819920969

Machine learning assisted evolutionary multi- and many- objective optimization
Machine learning assisted evolutionary multi- and many- objective optimization[electronic resource] /by Dhish Kumar Saxena ... [et al.]. - Singapore :Springer Nature Singapore :2024. - xv, 244 p. :ill. (some col.), digital ;24 cm. - Genetic and evolutionary computation,1932-0175. - Genetic and evolutionary computation..

Introduction -- Optimization Problems and Algorithms -- Existing Machine Learning Studies on Multi-objective Optimization -- Learning to Converge Better and Faster -- Learning to Diversify Better and Faster -- Learning to Simultaneously Converge and Diversify Better and Faster -- Learning to Understand the Problem Structure -- ML-Assisted Analysis of Pareto-optimal Front -- Further Machine Learning Assisted Enhancements -- Conclusions.

This book focuses on machine learning (ML) assisted evolutionary multi- and many-objective optimization (EMâO) EMâO algorithms, namely EMâOAs, iteratively evolve a set of solutions towards a good Pareto Front approximation. The availability of multiple solution sets over successive generations makes EMâOAs amenable to application of ML for different pursuits. Recognizing the immense potential for ML-based enhancements in the EMâO domain, this book intends to serve as an exclusive resource for both domain novices and the experienced researchers and practitioners. To achieve this goal, the book first covers the foundations of optimization, including problem and algorithm types. Then, well-structured chapters present some of the key studies on ML-based enhancements in the EMâO domain, systematically addressing important aspects. These include learning to understand the problem structure, converge better, diversify better, simultaneously converge and diversify better, and analyze the Pareto Front. In doing so, this book broadly summarizes the literature, beginning with foundational work on innovization (2003) and objective reduction (2006), and extending to the most recently proposed innovized progress operators (2021-23) It also highlights the utility of ML interventions in the search, post-optimality, and decision-making phases pertaining to the use of EMâOAs. Finally, this book shares insightful perspectives on the future potential for ML based enhancements in the EMâOA domain. To aid readers, the book includes working codes for the developed algorithms. This book will not only strengthen this emergent theme but also encourage ML researchers to develop more efficient and scalable methods that cater to the requirements of the EMâOA domain. It serves as an inspiration for further research and applications at the synergistic intersection of EMâOA and ML domains.

ISBN: 9789819920969

Standard No.: 10.1007/978-981-99-2096-9doiSubjects--Topical Terms:

517763
Mathematical optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Machine learning assisted evolutionary multi- and many- objective optimization
LDR:03432nmm a2200337 a 4500 001 2371228
003 DE-He213
005 20240517125435.0
006 m d
007 cr nn 008maaau
008 241127s2024 si s 0 eng d
020 $a 9789819920969 $q (electronic bk.)
020 $a 9789819920952 $q (paper)
024 7 $a 10.1007/978-981-99-2096-9 $2 doi
035 $a 978-981-99-2096-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA402.5
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 519.6 $2 23
090 $a QA402.5 $b .M149 2024
245 0 0 $a Machine learning assisted evolutionary multi- and many- objective optimization $h [electronic resource] / $c by Dhish Kumar Saxena ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2024.
300 $a xv, 244 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Genetic and evolutionary computation, $x 1932-0175
505 0 $a Introduction -- Optimization Problems and Algorithms -- Existing Machine Learning Studies on Multi-objective Optimization -- Learning to Converge Better and Faster -- Learning to Diversify Better and Faster -- Learning to Simultaneously Converge and Diversify Better and Faster -- Learning to Understand the Problem Structure -- ML-Assisted Analysis of Pareto-optimal Front -- Further Machine Learning Assisted Enhancements -- Conclusions.
520 $a This book focuses on machine learning (ML) assisted evolutionary multi- and many-objective optimization (EMâO) EMâO algorithms, namely EMâOAs, iteratively evolve a set of solutions towards a good Pareto Front approximation. The availability of multiple solution sets over successive generations makes EMâOAs amenable to application of ML for different pursuits. Recognizing the immense potential for ML-based enhancements in the EMâO domain, this book intends to serve as an exclusive resource for both domain novices and the experienced researchers and practitioners. To achieve this goal, the book first covers the foundations of optimization, including problem and algorithm types. Then, well-structured chapters present some of the key studies on ML-based enhancements in the EMâO domain, systematically addressing important aspects. These include learning to understand the problem structure, converge better, diversify better, simultaneously converge and diversify better, and analyze the Pareto Front. In doing so, this book broadly summarizes the literature, beginning with foundational work on innovization (2003) and objective reduction (2006), and extending to the most recently proposed innovized progress operators (2021-23) It also highlights the utility of ML interventions in the search, post-optimality, and decision-making phases pertaining to the use of EMâOAs. Finally, this book shares insightful perspectives on the future potential for ML based enhancements in the EMâOA domain. To aid readers, the book includes working codes for the developed algorithms. This book will not only strengthen this emergent theme but also encourage ML researchers to develop more efficient and scalable methods that cater to the requirements of the EMâOA domain. It serves as an inspiration for further research and applications at the synergistic intersection of EMâOA and ML domains.
650 0 $a Mathematical optimization. $3 517763
650 0 $a Evolutionary computation. $3 582189
650 0 $a Machine learning. $3 533906
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Computational Intelligence. $3 1001631
700 1 $a Saxena, Dhish Kumar. $3 3717089
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Genetic and evolutionary computation. $3 2062544
856 4 0 $u https://doi.org/10.1007/978-981-99-2096-9
950 $a Computer Science (SpringerNature-11645)