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 到查詢結果 [ subject:"Natural computation" ]

切換: 標籤 | MARC模式 | ISBD

Bioinspired heuristics for optimization

Talbi, El-Ghazali.

FindBook

Google Book

Amazon

博客來

Bioinspired heuristics for optimization

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bioinspired heuristics for optimization/ edited by El-Ghazali Talbi, Amir Nakib.
其他作者:	Talbi, El-Ghazali.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	viii, 314 p. :ill., digital ;24 cm.
內容註:	Possibilistic Framework for Multi-objective Optimization under Uncertainty -- Solving the Uncapacitated Single Allocation p-Hub Median Problem on GPU. Phase Equilibrium Description of a Supercritical Extraction System using Metaheuristic Optimization Algorithms -- Intrusion Detection System based on a behavioral approach -- A new hybrid method to solve the multi-objective optimization problem for a composite hat-stiffened panel -- Storage yard management: modelling and solving -- Multi-capacitated location problem : A new resolution method combining exact and heuristic approaches based on set partitioning -- Application of genetic algorithm for solving bilevel linear programming problems -- Adapted Bin-Packing algorithm for the yard optimization problem -- Hidden Markov Model classifier for the adaptive ACS-TSP pheromone parameters.
Contained By:	Springer eBooks
標題:	Natural computation - Congresses. -
電子資源:	https://doi.org/10.1007/978-3-319-95104-1
ISBN:	9783319951041

Bioinspired heuristics for optimization
Bioinspired heuristics for optimization[electronic resource] /edited by El-Ghazali Talbi, Amir Nakib. - Cham :Springer International Publishing :2019. - viii, 314 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.7741860-949X ;. - Studies in computational intelligence ;v.774..

Possibilistic Framework for Multi-objective Optimization under Uncertainty -- Solving the Uncapacitated Single Allocation p-Hub Median Problem on GPU. Phase Equilibrium Description of a Supercritical Extraction System using Metaheuristic Optimization Algorithms -- Intrusion Detection System based on a behavioral approach -- A new hybrid method to solve the multi-objective optimization problem for a composite hat-stiffened panel -- Storage yard management: modelling and solving -- Multi-capacitated location problem : A new resolution method combining exact and heuristic approaches based on set partitioning -- Application of genetic algorithm for solving bilevel linear programming problems -- Adapted Bin-Packing algorithm for the yard optimization problem -- Hidden Markov Model classifier for the adaptive ACS-TSP pheromone parameters.

This book presents recent research on bioinspired heuristics for optimization. Learning- based and black-box optimization exhibit some properties of intrinsic parallelization, and can be used for various optimizations problems. Featuring the most relevant work presented at the 6th International Conference on Metaheuristics and Nature Inspired Computing, held at Marrakech (Morocco) from 27th to 31st October 2016, the book presents solutions, methods, algorithms, case studies, and software. It is a valuable resource for research academics and industrial practitioners.

ISBN: 9783319951041

Standard No.: 10.1007/978-3-319-95104-1doiSubjects--Topical Terms:

1085469
Natural computation
--Congresses.

LC Class. No.: QA76.9.N37 / B56 2019

Dewey Class. No.: 006.38

Bioinspired heuristics for optimization
LDR:02484nmm a2200337 a 4500 001 2192218
003 DE-He213
005 20191220130541.0
006 m d
007 cr nn 008maaau
008 200506s2019 gw s 0 eng d
020 $a 9783319951041 $q (electronic bk.)
020 $a 9783319951034 $q (paper)
024 7 $a 10.1007/978-3-319-95104-1 $2 doi
035 $a 978-3-319-95104-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.N37 $b B56 2019
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.38 $2 23
090 $a QA76.9.N37 $b B615 2019
245 0 0 $a Bioinspired heuristics for optimization $h [electronic resource] / $c edited by El-Ghazali Talbi, Amir Nakib.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a viii, 314 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.774
505 0 $a Possibilistic Framework for Multi-objective Optimization under Uncertainty -- Solving the Uncapacitated Single Allocation p-Hub Median Problem on GPU. Phase Equilibrium Description of a Supercritical Extraction System using Metaheuristic Optimization Algorithms -- Intrusion Detection System based on a behavioral approach -- A new hybrid method to solve the multi-objective optimization problem for a composite hat-stiffened panel -- Storage yard management: modelling and solving -- Multi-capacitated location problem : A new resolution method combining exact and heuristic approaches based on set partitioning -- Application of genetic algorithm for solving bilevel linear programming problems -- Adapted Bin-Packing algorithm for the yard optimization problem -- Hidden Markov Model classifier for the adaptive ACS-TSP pheromone parameters.
520 $a This book presents recent research on bioinspired heuristics for optimization. Learning- based and black-box optimization exhibit some properties of intrinsic parallelization, and can be used for various optimizations problems. Featuring the most relevant work presented at the 6th International Conference on Metaheuristics and Nature Inspired Computing, held at Marrakech (Morocco) from 27th to 31st October 2016, the book presents solutions, methods, algorithms, case studies, and software. It is a valuable resource for research academics and industrial practitioners.
650 0 $a Natural computation $v Congresses. $3 1085469
650 0 $a Mathematical optimization $x Congresses. $3 647613
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Talbi, El-Ghazali. $3 2179359
700 1 $a Nakib, Amir. $3 3227191
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Studies in computational intelligence ; $v v.774. $3 3412341
856 4 0 $u https://doi.org/10.1007/978-3-319-95104-1
950 $a Intelligent Technologies and Robotics (Springer-42732)