Genetic Programming for Production Scheduling: An Evolutionary Learning Approach - Info and Reading Options

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Intro -- Foreword -- Preface -- Acknowledgements -- Contents -- Acronyms -- List of Figures -- List of Tables -- Part I Introduction -- 1 Introduction -- 1.1 Production Scheduling -- 1.2 Machine Learning -- 1.2.1 Training Set and Test Set -- 1.2.2 Types of Machine Learning Tasks -- 1.2.3 Machine Learning Paradigms -- 1.3 Evolutionary Learning and Genetic Programming -- 1.3.1 Evolutionary Computation -- 1.3.2 Genetic Programming -- 1.4 Framework of Genetic Programming for Production Scheduling -- 1.5 Interpretable Machine Learning -- 1.6 Terminology -- 1.7 Organisation of the Book -- 2 Preliminaries -- 2.1 Job Shop Scheduling -- 2.2 Exact, Heuristic, and Hyper-heuristic Approaches -- 2.3 Hyper-heuristics in Evolutionary Learning -- 2.4 Scheduling Heuristics for Job Shop Scheduling -- 2.5 Genetic Programming for Production Scheduling Heuristics -- 2.5.1 Advantages of Genetic Programming for Production Scheduling -- 2.5.2 Overall Process of Genetic Programming for Job Shop Scheduling -- 2.5.3 Extracting High-Level Heuristic from Low-Level Heuristics -- 2.6 Evaluations of Genetic Programming Hyper-heuristics -- 2.7 Chapter Summary -- Part II Genetic Programming for Static Production Scheduling Problems -- 3 Learning Schedule Construction Heuristics -- 3.1 Challenges and Motivations -- 3.2 Algorithm Design and Details -- 3.2.1 Meta-algorithm for Schedule Construction -- 3.2.2 Representations of Scheduling Construction Heuristics -- 3.2.3 Fitness Evaluation -- 3.2.4 Proposed Genetic Programming Algorithm -- 3.3 Empirical Study -- 3.3.1 Parameter Settings -- 3.3.2 Datasets -- 3.3.3 Performance of Learned Heuristics -- 3.3.4 Further Analyses -- 3.4 Chapter Summary -- 4 Learning Schedule Improvement Heuristics -- 4.1 Challenges and Motivations -- 4.2 Algorithm Design and Details -- 4.2.1 Meta-algorithm for Iterative Dispatching Rules

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