Metaheuristics for finding multiple solutions / Mike Preuss, Michael G. Epitropakis, Xiaodong Li, Jonathan E. Fieldsend, editors.
Preuss, Mike, editor.; Epitropakis, Michael G., editor.; Li, Xiaodong, Ph. D., editor.; Fieldsend, Jonathan E., editor.
2021
QA76.9.A43
Available Online

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Title Metaheuristics for finding multiple solutions / Mike Preuss, Michael G. Epitropakis, Xiaodong Li, Jonathan E. Fieldsend, editors.
ISBN 9783030795535 (electronic bk.)
3030795535 (electronic bk.)
3030795527
9783030795528
DOI https://doi.org/10.1007/978-3-030-79553-5
Publication Details Cham, Switzerland : Springer, 2021.
Language English
Description 1 online resource
Item Number 10.1007/978-3-030-79553-5 doi
Call Number QA76.9.A43
Dewey Decimal Classification 518/.1
Summary This 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.
Access Note Access limited to authorized users.
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PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed October 27, 2021).
Added Author Preuss, Mike, editor.
Epitropakis, Michael G., editor.
Li, Xiaodong, Ph. D., editor.
Fieldsend, Jonathan E., editor.
Series Natural computing series.
Available in Other Form Metaheuristics for finding multiple solutions.
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Introduction
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.

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