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008			211022s2021 sz | s |||| 0|eng d
020			_a9783030795535
024	7		_a10.1007/978-3-030-79553-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9 .A43 _b2021 EB
245	1	0	_aMetaheuristics for Finding Multiple Solutions _cedited by Mike Preuss, Michael G. Epitropakis, Xiaodong Li, Jonathan E. Fieldsend.
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publising _c2021
300			_a1 recurso en línea (XII, 315 páginas) _b115 ilustraciones, 75 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aNatural Computing Series
490	0		_aComputer Science (SpringerNature-11645)
490	0		_aComputer Science (R0) (SpringerNature-43710)
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	3		_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 by a collection of open research questions and possible research directions that may be tackled in the future.
988			_aSpringer_Computer_2021
650		7	_2embne _9467159 _aProgramación metaheurística
700	1		_aPreuss, Mike _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aEpitropakis, Michael G _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aLi, Xiaodong _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aFieldsend, Jonathan E _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783030795528
776	0	8	_iPrinted edition: _z9783030795542
776	0	8	_iPrinted edition: _z9783030795559
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-79553-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2022 _dz _eh _zSI