東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Metaheuristics for finding multiple ...

Preuss, Mike.

FindBook

Google Book

Amazon

博客來

Metaheuristics for finding multiple solutions

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Metaheuristics for finding multiple solutions/ edited by Mike Preuss ... [et al.].
其他作者:	Preuss, Mike.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	xii, 315 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Metaheuristics. -
電子資源:	https://doi.org/10.1007/978-3-030-79553-5
ISBN:	9783030795535

Metaheuristics for finding multiple solutions
Metaheuristics for finding multiple solutions[electronic resource] /edited by Mike Preuss ... [et al.]. - Cham :Springer International Publishing :2021. - xii, 315 p. :ill., digital ;24 cm. - Natural computing series. - Natural computing series..

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.

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.

ISBN: 9783030795535

Standard No.: 10.1007/978-3-030-79553-5doiSubjects--Topical Terms:

2206834
Metaheuristics.

LC Class. No.: QA76.9.A43 / M47 2021

Dewey Class. No.: 518.1

Metaheuristics for finding multiple solutions
LDR:03373nmm a2200337 a 4500 001 2253483
003 DE-He213
005 20211022225553.0
006 m d
007 cr nn 008maaau
008 220327s2021 sz s 0 eng d
020 $a 9783030795535 $q (electronic bk.)
020 $a 9783030795528 $q (paper)
024 7 $a 10.1007/978-3-030-79553-5 $2 doi
035 $a 978-3-030-79553-5
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.A43 $b M47 2021
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 518.1 $2 23
090 $a QA76.9.A43 $b M587 2021
245 0 0 $a Metaheuristics for finding multiple solutions $h [electronic resource] / $c edited by Mike Preuss ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xii, 315 p. : $b ill., digital ; $c 24 cm.
490 1 $a Natural computing series
505 0 $a 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.
520 $a 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.
650 0 $a Metaheuristics. $3 2206834
650 0 $a Mathematical optimization. $3 517763
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Theory of Computation. $3 892514
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Operations Research/Decision Theory. $3 890895
650 2 4 $a Optimization. $3 891104
700 1 $a Preuss, Mike. $3 2162360
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Natural computing series. $3 2057566
856 4 0 $u https://doi.org/10.1007/978-3-030-79553-5
950 $a Computer Science (SpringerNature-11645)