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Evolutionary Computation in Combinatorial Optimization

Overview of attention for book
Cover of 'Evolutionary Computation in Combinatorial Optimization'

Table of Contents

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    Book Overview
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    Chapter 1 Better Runtime Guarantees via Stochastic Domination
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    Chapter 2 On the Fractal Nature of Local Optima Networks
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    Chapter 3 How Perturbation Strength Shapes the Global Structure of TSP Fitness Landscapes
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    Chapter 4 Worst Improvement Based Iterated Local Search
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    Chapter 5 Automatic Grammar-Based Design of Heuristic Algorithms for Unconstrained Binary Quadratic Programming
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    Chapter 6 Automatic Algorithm Configuration for the Permutation Flow Shop Scheduling Problem Minimizing Total Completion Time
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    Chapter 7 Data Clustering Using Grouping Hyper-heuristics
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    Chapter 8 Reference Point Adaption Method for Genetic Programming Hyper-Heuristic in Many-Objective Job Shop Scheduling
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    Chapter 9 MOEA/DEP: An Algebraic Decomposition-Based Evolutionary Algorithm for the Multiobjective Permutation Flowshop Scheduling Problem
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    Chapter 10 An Evolutionary Algorithm with Practitioner’s-Knowledge-Based Operators for the Inventory Routing Problem
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    Chapter 11 A Multistart Alternating Tabu Search for Commercial Districting
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    Chapter 12 An Ant Colony Approach for the Winner Determination Problem
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    Chapter 13 Erratum to: On the Fractal Nature of Local Optima Networks
Attention for Chapter 7: Data Clustering Using Grouping Hyper-heuristics
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8 Mendeley
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Chapter title
Data Clustering Using Grouping Hyper-heuristics
Chapter number 7
Book title
Evolutionary Computation in Combinatorial Optimization
Published by
Springer, Cham, April 2018
DOI 10.1007/978-3-319-77449-7_7
Book ISBNs
978-3-31-977448-0, 978-3-31-977449-7
Authors

Anas Elhag, Ender Özcan

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 38%
Lecturer 2 25%
Lecturer > Senior Lecturer 1 13%
Unknown 2 25%
Readers by discipline Count As %
Computer Science 5 63%
Engineering 1 13%
Unknown 2 25%

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