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001440520 003__ OCoLC
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001440520 0247_ $$a10.1007/978-3-030-79553-5$$2doi
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001440520 24500 $$aMetaheuristics for finding multiple solutions /$$cMike Preuss, Michael G. Epitropakis, Xiaodong Li, Jonathan E. Fieldsend, editors.
001440520 260__ $$aCham, Switzerland :$$bSpringer,$$c2021.
001440520 300__ $$a1 online resource
001440520 336__ $$atext$$btxt$$2rdacontent
001440520 337__ $$acomputer$$bc$$2rdamedia
001440520 338__ $$aonline resource$$bcr$$2rdacarrier
001440520 347__ $$atext file
001440520 347__ $$bPDF
001440520 4901_ $$aNatural computing series
001440520 5050_ $$aIntroduction -- Theoretical Studies and Analysis of Niching Methods -- Parameter Adaptation in Niching Methods -- Lowering Computational Cost -- Scalability -- Performance Metrics -- Comparative Studies -- Methods for Machine Learning and Clustering -- Real-World Applications.
001440520 506__ $$aAccess limited to authorized users.
001440520 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 by a collection of open research questions and possible research directions that may be tackled in the future.
001440520 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 27, 2021).
001440520 650_0 $$aMetaheuristics.
001440520 650_0 $$aMathematical optimization.
001440520 650_6 $$aMétaheuristiques.
001440520 650_6 $$aOptimisation mathématique.
001440520 655_0 $$aElectronic books.
001440520 7001_ $$aPreuss, Mike,$$eeditor.
001440520 7001_ $$aEpitropakis, Michael G.,$$eeditor.
001440520 7001_ $$aLi, Xiaodong,$$cPh. D.,$$eeditor.
001440520 7001_ $$aFieldsend, Jonathan E.,$$eeditor.
001440520 77608 $$iPrint version:$$tMetaheuristics for finding multiple solutions.$$dCham, Switzerland : Springer, 2021$$z3030795527$$z9783030795528$$w(OCoLC)1252961574
001440520 830_0 $$aNatural computing series.
001440520 852__ $$bebk
001440520 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-79553-5$$zOnline Access$$91397441.1
001440520 909CO $$ooai:library.usi.edu:1440520$$pGLOBAL_SET
001440520 980__ $$aBIB
001440520 980__ $$aEBOOK
001440520 982__ $$aEbook
001440520 983__ $$aOnline
001440520 994__ $$a92$$bISE