國立虎尾科技大學 |

Evolutionary and Swarm Intelligence Algorithms

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Evolutionary and Swarm Intelligence Algorithms/ edited by Jagdish Chand Bansal, Pramod Kumar Singh, Nikhil R. Pal.
other author:	Bansal, Jagdish Chand.
Description:	X, 190 p. 57 illus., 21 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational intelligence. -
Online resource:	https://doi.org/10.1007/978-3-319-91341-4
ISBN:	9783319913414

Evolutionary and Swarm Intelligence Algorithms
Evolutionary and Swarm Intelligence Algorithms[electronic resource] /edited by Jagdish Chand Bansal, Pramod Kumar Singh, Nikhil R. Pal. - 1st ed. 2019. - X, 190 p. 57 illus., 21 illus. in color.online resource. - Studies in Computational Intelligence,7791860-949X ;. - Studies in Computational Intelligence,564.

Swarm and Evolutionary Computation -- Particle Swarm Optimization -- Artificial Bee Colony Algorithm Variants and Its Application to Colormap Quantization -- Spider Monkey Optimization Algorithm -- Genetic Algorithm and Its Advances in Embracing Memetics -- Constrained Multi-Objective Evolutionary Algorithm -- Genetic Programming for Classification and Feature Selection -- Genetic Programming for Job Shop Scheduling -- Evolutionary Fuzzy Systems: A Case Study for Intrusion Detection Systems.

This book is a delight for academics, researchers and professionals working in evolutionary and swarm computing, computational intelligence, machine learning and engineering design, as well as search and optimization in general. It provides an introduction to the design and development of a number of popular and recent swarm and evolutionary algorithms with a focus on their applications in engineering problems in diverse domains. The topics discussed include particle swarm optimization, the artificial bee colony algorithm, Spider Monkey optimization algorithm, genetic algorithms, constrained multi-objective evolutionary algorithms, genetic programming, and evolutionary fuzzy systems. A friendly and informative treatment of the topics makes this book an ideal reference for beginners and those with experience alike.

ISBN: 9783319913414

Standard No.: 10.1007/978-3-319-91341-4doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Evolutionary and Swarm Intelligence Algorithms
LDR:02811nam a22004095i 4500 001 1008331
003 DE-He213
005 20200702110652.0
007 cr nn 008mamaa
008 210106s2019 gw | s |||| 0|eng d
020 $a 9783319913414 $9 978-3-319-91341-4
024 7 $a 10.1007/978-3-319-91341-4 $2 doi
035 $a 978-3-319-91341-4
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
245 1 0 $a Evolutionary and Swarm Intelligence Algorithms $h [electronic resource] / $c edited by Jagdish Chand Bansal, Pramod Kumar Singh, Nikhil R. Pal.
250 $a 1st ed. 2019.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a X, 190 p. 57 illus., 21 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Studies in Computational Intelligence, $x 1860-949X ; $v 779
505 0 $a Swarm and Evolutionary Computation -- Particle Swarm Optimization -- Artificial Bee Colony Algorithm Variants and Its Application to Colormap Quantization -- Spider Monkey Optimization Algorithm -- Genetic Algorithm and Its Advances in Embracing Memetics -- Constrained Multi-Objective Evolutionary Algorithm -- Genetic Programming for Classification and Feature Selection -- Genetic Programming for Job Shop Scheduling -- Evolutionary Fuzzy Systems: A Case Study for Intrusion Detection Systems.
520 $a This book is a delight for academics, researchers and professionals working in evolutionary and swarm computing, computational intelligence, machine learning and engineering design, as well as search and optimization in general. It provides an introduction to the design and development of a number of popular and recent swarm and evolutionary algorithms with a focus on their applications in engineering problems in diverse domains. The topics discussed include particle swarm optimization, the artificial bee colony algorithm, Spider Monkey optimization algorithm, genetic algorithms, constrained multi-objective evolutionary algorithms, genetic programming, and evolutionary fuzzy systems. A friendly and informative treatment of the topics makes this book an ideal reference for beginners and those with experience alike.
650 0 $a Computational intelligence. $3 568984
650 0 $a Artificial intelligence. $3 559380
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Artificial Intelligence. $3 646849
700 1 $a Bansal, Jagdish Chand. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1073553
700 1 $a Singh, Pramod Kumar. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1302143
700 1 $a Pal, Nikhil R. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 676653
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319913391
776 0 8 $i Printed edition: $z 9783319913407
776 0 8 $i Printed edition: $z 9783030082291
830 0 $a Studies in Computational Intelligence, $x 1860-949X ; $v 564 $3 1253640
856 4 0 $u https://doi.org/10.1007/978-3-319-91341-4
912 $a ZDB-2-INR
912 $a ZDB-2-SXIT
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)
950 $a Intelligent Technologies and Robotics (R0) (SpringerNature-43728)