Optimization and Machine Learning. Patrick Siarry

      

Table of Contents

      1  Cover

      2  Title Page

      3  Copyright

      4  Introduction

      5  PART 1 Optimization 1 Vehicle Routing Problems with Loading Constraints: An Overview of Variants and Solution Methods 1.1. Introduction 1.2. The capacitated vehicle routing problem with two-dimensional loading constraints 1.3. The capacitated vehicle routing problem with three-dimensional loading constraints 1.4. Perspectives on future research 1.5. References 2 MAS-aware Approach for QoS-based IoT Workflow Scheduling in Fog-Cloud Computing 2.1. Introduction 2.2. Related works 2.3. Problem formulation 2.4. MAS-GA-based approach for IoT workflow scheduling 2.5. GA-based workflow scheduling plan 2.6. Experimental study and analysis of the results 2.7. Conclusion 2.8. References 3 Solving Feature Selection Problems Built on Population-based Metaheuristic Algorithms 3.1. Introduction 3.2. Algorithm inspiration 3.3. Mathematical modeling 3.4. Theoretical fundamentals of feature selection 3.5. Mathematical modeling of the feature selection optimization problem 3.6. Adaptation of metaheuristics for optimization in a binary search space 3.7. Adaptation of the grey wolf algorithm to feature selection in a binary search space 3.8. Experimental implementation of bGWO1 and bGWO2 and discussion 3.9. Conclusion 3.10. References 4 Solving the Mixed-model Assembly Line Balancing Problem by using a Hybrid Reactive Greedy Randomized Adaptive Search Procedure 4.1. Introduction 4.2. Related works from the literature 4.3. Problem description and mathematical formulation 4.4. Basic greedy randomized adaptive search procedure 4.5. Reactive greedy randomized adaptive search procedure 4.6. Hybrid reactive greedy randomized adaptive search procedure for the mixed model assembly line balancing problem type-2 4.7. Experimental examples 4.8. Conclusion 4.9. References

      6  PART 2 Machine Learning 5 An Interactive Attention Network with Stacked Ensemble Machine Learning Models for Recommendations 5.1. Introduction 5.2. Related work 5.3. Interactive personalized recommender 5.4. Experimental settings 5.5. Experiments and discussion 5.6. Conclusion 5.7. References 6 A Comparison of Machine Learning and Deep Learning Models with Advanced Word Embeddings: The Case of Internal Audit Reports 6.1. Introduction 6.2. Related work 6.3. Experiments and evaluation 6.4. Conclusion and future work 6.5. References 7 Hybrid Approach based on Multi-agent System and Fuzzy Logic for Mobile Robot Autonomous Navigation 7.1. Introduction 7.2. Related works 7.3. Problem position 7.4. Developed control architecture 7.5. Navigation principle by fuzzy logic 7.6. Simulation and results 7.7. Conclusion 7.8. References 8 Intrusion Detection with Neural Networks: A Tutorial 8.1. Introduction 8.2. Dataset analysis 8.3. Data preparation