Optimization and Machine Learning

Optimization and Machine Learning

by Rachid Chelouah, Patrick Siarry
Released April 2022
Publisher(s): Wiley-ISTE
ISBN: 9781789450712

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Book description

Machine learning and optimization techniques are revolutionizing our world. Other types of information technology have not progressed as rapidly in recent years, in terms of real impact. The aim of this book is to present some of the innovative techniques in the field of optimization and machine learning, and to demonstrate how to apply them in the fields of engineering.

Optimization and Machine Learning presents modern advances in the selection, configuration and engineering of algorithms that rely on machine learning and optimization. The first part of the book is dedicated to applications where optimization plays a major role, and the second part describes and implements several applications that are mainly based on machine learning techniques. The methods addressed in these chapters are compared against their competitors, and their effectiveness in their chosen field of application is illustrated.

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Introduction
  5. PART 1 Optimization
    1. 1 Vehicle Routing Problems with Loading Constraints: An Overview of Variants and Solution Methods
      1. 1.1. Introduction
      2. 1.2. The capacitated vehicle routing problem with two-dimensional loading constraints
      3. 1.3. The capacitated vehicle routing problem with three-dimensional loading constraints
      4. 1.4. Perspectives on future research
      5. 1.5. References
    2. 2 MAS-aware Approach for QoS-based IoT Workflow Scheduling in Fog-Cloud Computing
      1. 2.1. Introduction
      2. 2.2. Related works
      3. 2.3. Problem formulation
      4. 2.4. MAS-GA-based approach for IoT workflow scheduling
      5. 2.5. GA-based workflow scheduling plan
      6. 2.6. Experimental study and analysis of the results
      7. 2.7. Conclusion
      8. 2.8. References
    3. 3 Solving Feature Selection Problems Built on Population-based Metaheuristic Algorithms
      1. 3.1. Introduction
      2. 3.2. Algorithm inspiration
      3. 3.3. Mathematical modeling
      4. 3.4. Theoretical fundamentals of feature selection
      5. 3.5. Mathematical modeling of the feature selection optimization problem
      6. 3.6. Adaptation of metaheuristics for optimization in a binary search space
      7. 3.7. Adaptation of the grey wolf algorithm to feature selection in a binary search space
      8. 3.8. Experimental implementation of bGWO1 and bGWO2 and discussion
      9. 3.9. Conclusion
      10. 3.10. References
    4. 4 Solving the Mixed-model Assembly Line Balancing Problem by using a Hybrid Reactive Greedy Randomized Adaptive Search Procedure
      1. 4.1. Introduction
      2. 4.2. Related works from the literature
      3. 4.3. Problem description and mathematical formulation
      4. 4.4. Basic greedy randomized adaptive search procedure
      5. 4.5. Reactive greedy randomized adaptive search procedure
      6. 4.6. Hybrid reactive greedy randomized adaptive search procedure for the mixed model assembly line balancing problem type-2
      7. 4.7. Experimental examples
      8. 4.8. Conclusion
      9. 4.9. References
  6. PART 2 Machine Learning
    1. 5 An Interactive Attention Network with Stacked Ensemble Machine Learning Models for Recommendations
      1. 5.1. Introduction
      2. 5.2. Related work
      3. 5.3. Interactive personalized recommender
      4. 5.4. Experimental settings
      5. 5.5. Experiments and discussion
      6. 5.6. Conclusion
      7. 5.7. References
    2. 6 A Comparison of Machine Learning and Deep Learning Models with Advanced Word Embeddings: The Case of Internal Audit Reports
      1. 6.1. Introduction
      2. 6.2. Related work
      3. 6.3. Experiments and evaluation
      4. 6.4. Conclusion and future work
      5. 6.5. References
    3. 7 Hybrid Approach based on Multi-agent System and Fuzzy Logic for Mobile Robot Autonomous Navigation
      1. 7.1. Introduction
      2. 7.2. Related works
      3. 7.3. Problem position
      4. 7.4. Developed control architecture
      5. 7.5. Navigation principle by fuzzy logic
      6. 7.6. Simulation and results
      7. 7.7. Conclusion
      8. 7.8. References
    4. 8 Intrusion Detection with Neural Networks: A Tutorial
      1. 8.1. Introduction
      2. 8.2. Dataset analysis
      3. 8.3. Data preparation
      4. 8.4. Feature selection
      5. 8.5. Model design
      6. 8.6. Results comparison
      7. 8.7. Deployment in a network
      8. 8.8. Future work
      9. 8.9. References
  7. List of Authors
  8. Index
  9. End User License Agreement

Product information

  • Title: Optimization and Machine Learning
  • Author(s): Rachid Chelouah, Patrick Siarry
  • Release date: April 2022
  • Publisher(s): Wiley-ISTE
  • ISBN: 9781789450712