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Advancements in applied metaheuristic computing

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Advancements in applied metaheuristic computing/ Nilanjan Dey, editor.
other author:	Dey, Nilanjan,
Published:	Hershey, Pennsylvania :IGI Global, : [2018],
Description:	1 online resource (xxi, 335 p.)
Subject:	Systems engineering - Data processing. -
Online resource:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-4151-6
ISBN:	9781522541523 (ebook)

Advancements in applied metaheuristic computing
Advancements in applied metaheuristic computing[electronic resource] /Nilanjan Dey, editor. - Hershey, Pennsylvania :IGI Global,[2018] - 1 online resource (xxi, 335 p.)

Includes bibliographical references and index.

Section 1. Meta-heuristic optimization-algorithms-based advanced applications. Chapter 1. Multi-objective optimal power flow using metaheuristic optimization algorithms with unified power flow controller to enhance the power system performance ; Chapter 2. Analyzing and predicting the QoS of traffic in wiMAX network using gene expression programming ; Chapter 3. Chaotic differential-evolution-based fuzzy contrast stretching method ; Chapter 4. Protein motif comparator using bio-inspired two-way K-means ; Chapter 5. Metaheuristics in manufacturing: predictive modeling of tool wear in machining using genetic programming ; Chapter 6. Intelligent computing in medical imaging: a study ; Chapter 7. Effect of SMES unit in AGC of an interconnected multi-area thermal power system with ACO-tuned PID controller ; Chapter 8. Meta-heuristic algorithms in medical image segmentation: a review -- Section 2. Genetic algorithm applications. Chapter 9. Optimized crossover jumpX in genetic algorithm for general routing problems: a crossover survey and enhancement ; Chapter 10. On developing and performance evaluation of adaptive second order neural network with GA-based training (ASONN-GA) for financial time series prediction ; Chapter 11. Hybrid non-dominated sorting genetic algorithm: II-neural network approach.

Restricted to subscribers or individual electronic text purchasers.

"This book considers the foremost optimization algorithms in several applications. It deals primarily with methods and approaches that include meta-heuristics for further systems improvements. It grants substantial frameworks and the most contemporary empirical research outcomes in employing optimization algorithms"--

ISBN: 9781522541523 (ebook)Subjects--Topical Terms:

827643
Systems engineering
--Data processing.

LC Class. No.: TA168 / .A286 2018e

Dewey Class. No.: 006.3

Advancements in applied metaheuristic computing
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245 0 0 $a Advancements in applied metaheuristic computing $h [electronic resource] / $c Nilanjan Dey, editor.
260 $a Hershey, Pennsylvania : $b IGI Global, $c [2018]
300 $a 1 online resource (xxi, 335 p.)
504 $a Includes bibliographical references and index.
505 0 $a Section 1. Meta-heuristic optimization-algorithms-based advanced applications. Chapter 1. Multi-objective optimal power flow using metaheuristic optimization algorithms with unified power flow controller to enhance the power system performance ; Chapter 2. Analyzing and predicting the QoS of traffic in wiMAX network using gene expression programming ; Chapter 3. Chaotic differential-evolution-based fuzzy contrast stretching method ; Chapter 4. Protein motif comparator using bio-inspired two-way K-means ; Chapter 5. Metaheuristics in manufacturing: predictive modeling of tool wear in machining using genetic programming ; Chapter 6. Intelligent computing in medical imaging: a study ; Chapter 7. Effect of SMES unit in AGC of an interconnected multi-area thermal power system with ACO-tuned PID controller ; Chapter 8. Meta-heuristic algorithms in medical image segmentation: a review -- Section 2. Genetic algorithm applications. Chapter 9. Optimized crossover jumpX in genetic algorithm for general routing problems: a crossover survey and enhancement ; Chapter 10. On developing and performance evaluation of adaptive second order neural network with GA-based training (ASONN-GA) for financial time series prediction ; Chapter 11. Hybrid non-dominated sorting genetic algorithm: II-neural network approach.
506 $a Restricted to subscribers or individual electronic text purchasers.
520 3 $a "This book considers the foremost optimization algorithms in several applications. It deals primarily with methods and approaches that include meta-heuristics for further systems improvements. It grants substantial frameworks and the most contemporary empirical research outcomes in employing optimization algorithms"-- $c Provided by publisher.
650 0 $a Systems engineering $x Data processing. $3 827643
650 0 $a Heuristic algorithms. $3 837099
650 0 $a Mathematical optimization. $3 527675
650 0 $a Artificial intelligence. $3 559380
700 1 $a Dey, Nilanjan, $d 1984- $e author. $3 1087003
856 4 0 $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-4151-6