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001			978-3-030-79553-5
003			DE-He213
005			20240423125433.0
007			cr nn 008mamaa
008			211022s2021 sz | s |||| 0|eng d
020			_a9783030795535 _9978-3-030-79553-5
024	7		_a10.1007/978-3-030-79553-5 _2doi
050		4	_aQ334-342
050		4	_aTA347.A78
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072		7	_aCOM004000 _2bisacsh
072		7	_aUYQ _2thema
082	0	4	_a006.3 _223
245	1	0	_aMetaheuristics for Finding Multiple Solutions _h[electronic resource] / _cedited by Mike Preuss, Michael G. Epitropakis, Xiaodong Li, Jonathan E. Fieldsend.
250			_a1st ed. 2021.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2021.
300			_aXII, 315 p. 115 illus., 75 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		_aNatural Computing Series, _x2627-6461
505	0		_aIntroduction -- Theoretical Studies and Analysis of Niching Methods -- Parameter Adaptation in Niching Methods -- Lowering Computational Cost -- Scalability -- Performance Metrics -- Comparative Studies -- Methods for Machine Learning and Clustering -- Real-World Applications.
520			_aThis book presents the latest trends and developments in multimodal optimization and niching techniques. Most existing optimization methods are designed for locating a single global solution. However, in real-world settings, many problems are “multimodal” by nature, i.e., multiple satisfactory solutions exist. It may be desirable to locate several such solutions before deciding which one to use. Multimodal optimization has been the subject of intense study in the field of population-based meta-heuristic algorithms, e.g., evolutionary algorithms (EAs), for the past few decades. These multimodal optimization techniques are commonly referred to as “niching” methods, because of the nature-inspired “niching” effect that is induced to the solution population targeting at multiple optima. Many niching methods have been developed in the EA community. Some classic examples include crowding, fitness sharing, clearing, derating, restricted tournament selection, speciation, etc. Nevertheless, applying these niching methods to real-world multimodal problems often encounters significant challenges. To facilitate the advance of niching methods in facing these challenges, this edited book highlights the latest developments in niching methods. The included chapters touch on algorithmic improvements and developments, representation, and visualization issues, as well as new research directions, such as preference incorporation in decision making and new application areas. This edited book is a first of this kind specifically on the topic of niching techniques. This book will serve as a valuable reference book both for researchers and practitioners. Although chapters are written in a mutually independent way, Chapter 1 will help novice readers get an overview of the field. It describes the development of the field and its current state and provides a comparative analysis of the IEEE CEC and ACM GECCO niching competitions of recent years, followed bya collection of open research questions and possible research directions that may be tackled in the future.
650		0	_aArtificial intelligence.
650		0	_aComputer science.
650		0	_aComputational intelligence.
650		0	_aOperations research.
650		0	_aMathematical optimization.
650	1	4	_aArtificial Intelligence.
650	2	4	_aTheory of Computation.
650	2	4	_aComputational Intelligence.
650	2	4	_aOperations Research and Decision Theory.
650	2	4	_aOptimization.
700	1		_aPreuss, Mike. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aEpitropakis, Michael G. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aLi, Xiaodong. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aFieldsend, Jonathan E. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783030795528
776	0	8	_iPrinted edition: _z9783030795542
776	0	8	_iPrinted edition: _z9783030795559
830		0	_aNatural Computing Series, _x2627-6461
856	4	0	_uhttps://doi.org/10.1007/978-3-030-79553-5
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
942			_cSPRINGER
999			_c177917 _d177917