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.
Part 1. Optimization.
1. Vehicle Routing Problems with Loading Constraints: An Overview of Variants and Solution Methods, Ines Sbai and Saoussen Krichen.
2. MAS-aware Approach for QoS-based IoT Workflow Scheduling in Fog-Cloud Computing, Marwa Mokni and Sonia Yassa.
3. Solving Feature Selection Problems Built on Population-based Metaheuristic Algorithms, Mohamed Sassi.
4. Solving the Mixed-model Assembly Line Balancing Problem by using a Hybrid Reactive Greedy Randomized Adaptive Search Procedure, Belkharroubi Lakhdar and Khadidja Yahyaoui.
Part 2. Machine Learning.
5. An Interactive Attention Network with Stacked Ensemble Machine Learning Models for Recommendations, Ahlem Drif, SaadEddine Selmani and Hocine Cherifi.
6. A Comparison of Machine Learning and Deep Learning Models with Advanced Word Embeddings: The Case of Internal Audit Reports, Gustavo Fleury Soares and Induraj Pudhupattu Ramamurthy.
7. Hybrid Approach based on Multi-agent System and Fuzzy Logic for Mobile Robot Autonomous Navigation, Khadidja Yahyaoui.
8. Intrusion Detection with Neural Networks: A Tutorial, Alvise De’ Faveri Tron.
Rachid Chelouah has a PhD and a Doctorate of Sciences (Habilitation) from CY Cergy Paris University, France. His main research interests are data science optimization and artificial intelligence methods and their applications in various fields of IT engineering, health, energy and security.
Patrick Siarry is a professor in automatics and informatics at Paris-East Créteil University, France. His main research interests are the design of stochastic global optimization heuristics and their applications to various engineering fields. He has coordinated several books in the field of optimization.
Table of Contents
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