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Genetic Programming for Production Scheduling

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Cover of 'Genetic Programming for Production Scheduling'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Preliminaries
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    Chapter 3 Learning Schedule Construction Heuristics
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    Chapter 4 Learning Schedule Improvement
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    Chapter 5 Learning to Augment Operations Research Algorithms
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    Chapter 6 Representations with Multi-tree and Cooperative Coevolution
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    Chapter 7 Efficiency Improvement with Multi-fidelity Surrogates
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    Chapter 8 Search Space Reduction with Feature Selection
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    Chapter 9 Search Mechanism with Specialised Genetic Operators
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    Chapter 10 Learning Heuristics for Multi-objective Dynamic Production Scheduling Problems
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    Chapter 11 Cooperative Coevolution for Multi-objective Production Scheduling Problems
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    Chapter 12 Learning Scheduling Heuristics for Multi-objective Dynamic Flexible Job Shop Scheduling
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    Chapter 13 Multitask Learning in Hyper-Heuristic Domain with Dynamic Production Scheduling
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    Chapter 14 Adaptive Multitask Genetic Programming for Dynamic Job Shop Scheduling
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    Chapter 15 Surrogate-Assisted Multitask Genetic Programming for Learning Scheduling Heuristics
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    Chapter 16 Conclusions and Prospects
Attention for Chapter 5: Learning to Augment Operations Research Algorithms
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Chapter title
Learning to Augment Operations Research Algorithms
Chapter number 5
Book title
Genetic Programming for Production Scheduling
Published by
Springer, Singapore, November 2021
DOI 10.1007/978-981-16-4859-5_5
Book ISBNs
978-9-81-164858-8, 978-9-81-164859-5
Authors

Zhang, Fangfang, Nguyen, Su, Mei, Yi, Zhang, Mengjie

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