LEADER 04587nam 22006975 450 001 9910734096403321 005 20230719192319.0 010 $a3-030-79553-5 024 7 $a10.1007/978-3-030-79553-5 035 $a(CKB)5340000000068525 035 $a(MiAaPQ)EBC6789906 035 $a(Au-PeEL)EBL6789906 035 $a(OCoLC)1280458989 035 $a(DE-He213)978-3-030-79553-5 035 $a(PPN)25829695X 035 $a(EXLCZ)995340000000068525 100 $a20211022d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMetaheuristics for Finding Multiple Solutions /$fedited by Mike Preuss, Michael G. Epitropakis, Xiaodong Li, Jonathan E. Fieldsend 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (322 pages) 225 1 $aNatural Computing Series,$x2627-6461 311 $a3-030-79552-7 320 $aIncludes bibliographical references and index. 327 $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. 330 $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. 410 0$aNatural Computing Series,$x2627-6461 606 $aArtificial intelligence 606 $aComputer science 606 $aComputational intelligence 606 $aOperations research 606 $aMathematical optimization 606 $aArtificial Intelligence 606 $aTheory of Computation 606 $aComputational Intelligence 606 $aOperations Research and Decision Theory 606 $aOptimization 615 0$aArtificial intelligence. 615 0$aComputer science. 615 0$aComputational intelligence. 615 0$aOperations research. 615 0$aMathematical optimization. 615 14$aArtificial Intelligence. 615 24$aTheory of Computation. 615 24$aComputational Intelligence. 615 24$aOperations Research and Decision Theory. 615 24$aOptimization. 676 $a518.1 702 $aPreuss$b Mike 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910734096403321 996 $aMetaheuristics for finding multiple solutions$92899836 997 $aUNINA