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001 978-3-319-42978-6
003 DE-He213
005 20220801222805.0
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008 160809s2017 sz | s |||| 0|eng d
020 _a9783319429786
_9978-3-319-42978-6
024 7 _a10.1007/978-3-319-42978-6
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aRecent Advances in Evolutionary Multi-objective Optimization
_h[electronic resource] /
_cedited by Slim Bechikh, Rituparna Datta, Abhishek Gupta.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXII, 179 p. 42 illus., 27 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 _aAdaptation, Learning, and Optimization,
_x1867-4542 ;
_v20
505 0 _aMulti-objective Optimization: Classical and Evolutionary Approaches -- Dynamic Multi-objective Optimization using Evolutionary Algorithms: A Survey -- Evolutionary Bilevel Optimization: An Introduction and Recent Advances -- Many-objective Optimization using Evolutionary Algorithms: A Survey -- On the Emerging Notion of Evolutionary Multitasking: A Computational Analog of Cognitive Multitasking -- Practical Applications in Constrained Evolutionary Multi-objective Optimization.
520 _aThis book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-andcoming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include:< optimization in dynamic environments, multi-objective bilevel programming, handling high dimensionality under many objectives, and evolutionary multitasking. In addition to theory and methodology, this book describes several real-world applications from various domains, which will expose the readers to the versatility of evolutionary multi-objective optimization.
650 0 _aComputational intelligence.
_97716
650 0 _aArtificial intelligence.
_93407
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aArtificial Intelligence.
_93407
700 1 _aBechikh, Slim.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_963377
700 1 _aDatta, Rituparna.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_963378
700 1 _aGupta, Abhishek.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_963379
710 2 _aSpringerLink (Online service)
_963380
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319429779
776 0 8 _iPrinted edition:
_z9783319429793
776 0 8 _iPrinted edition:
_z9783319827094
830 0 _aAdaptation, Learning, and Optimization,
_x1867-4542 ;
_v20
_963381
856 4 0 _uhttps://doi.org/10.1007/978-3-319-42978-6
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c81158
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