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001			978-981-99-2096-9
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005			20251030112047.0
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008			240517s2024 si | s |||| 0|eng d
020			_a9789819920969 _9978-981-99-2096-9
024	7		_a10.1007/978-981-99-2096-9 _2doi
050		4	_aQ334-342
050		4	_aTA347.A78
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
072		7	_aUYQ _2thema
100	1		_aSaxena, Dhish Kumar. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _976975
245	1	0	_aMachine Learning Assisted Evolutionary Multi- and Many- Objective Optimization _h[electronic resource] / _cby Dhish Kumar Saxena, Sukrit Mittal, Kalyanmoy Deb, Erik D. Goodman.
250			_a1st ed. 2024.
264		1	_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2024.
300			_aXV, 244 p. 83 illus., 53 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aGenetic and Evolutionary Computation, _x1932-0175
505	0		_aIntroduction -- 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			_aThis 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	_aArtificial intelligence. _958441
650		0	_aMachine learning. _955059
650		0	_aComputational intelligence. _964052
650	1	4	_aArtificial Intelligence. _958441
650	2	4	_aMachine Learning. _955059
650	2	4	_aComputational Intelligence. _964052
700	1		_aMittal, Sukrit. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _976976
700	1		_aDeb, Kalyanmoy. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _976977
700	1		_aGoodman, Erik D. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _976978
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9789819920952
776	0	8	_iPrinted edition: _z9789819920976
776	0	8	_iPrinted edition: _z9789819920983
830		0	_aGenetic and Evolutionary Computation, _x1932-0175 _966950
856	4	0	_uhttps://doi.org/10.1007/978-981-99-2096-9
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
999			_c59934 _d59934