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Evolutionary Computation in Combinatorial Optimization

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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 13: Erratum to: On the Fractal Nature of Local Optima Networks
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Chapter title
Erratum to: On the Fractal Nature of Local Optima Networks
Chapter number 13
Book title
Evolutionary Computation in Combinatorial Optimization
Published by
Springer, Cham, April 2018
DOI 10.1007/978-3-319-77449-7_13
Book ISBNs
978-3-31-977448-0, 978-3-31-977449-7
Authors

Sarah L. Thomson, Sébastien Verel, Gabriela Ochoa, Nadarajen Veerapen, Paul McMenemy

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