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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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.
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Seitenzahl: 285
Veröffentlichungsjahr: 2022
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Table of Contents
Cover
Title Page
Copyright
Introduction
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
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
8.4. Feature selection
8.5. Model design
8.6. Results comparison
8.7. Deployment in a network
8.8. Future work
8.9. References
List of Authors
Index
End User License Agreement
Guide
Cover
Table of Contents
Title Page
Copyright
Introduction
Begin Reading
List of Authors
Index
End User License Agreement
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SCIENCES
Computer Science,
Field Directors – Valérie Berthé and Jean-Charles Pomerol
Operational Research and Decision, Subject Head – Patrick Siarry
Optimization and Machine Learning
Optimization for Machine Learning and Machine Learning for Optimization
Coordinated by
Rachid Chelouah
Patrick Siarry
First published 2022 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the under mentioned address:
ISTE Ltd27-37 St George’s RoadLondon SW19 4EUUK
www.iste.co.uk
John Wiley & Sons, Inc111 River StreetHoboken, NJ 07030USA
www.wiley.com
© ISTE Ltd 2022
The rights of Rachid Chelouah and Patrick Siarry to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.
Library of Congress Control Number: 2021949293
British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
ISBN 978-1-78945-071-2
ERC code:
PE1 Mathematics
PE1_19 Control theory and optimization
PE6 Computer Science and Informatics
PE6_11 Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video)
Introduction
Rachid CHELOUAH
CY Cergy Paris University, France
Machine learning is revolutionizing our world. It is difficult to conceive of any other information technology that has developed so rapidly in recent years, in terms of real impact.
The fields of machine learning and optimization are highly interwoven. Optimization problems form the core of machine learning methods and modern optimization algorithms are using machine learning more and more to improve their efficiency.
Machine learning has applications in all areas of science. There are many learning methods, each of which uses a different algorithmic structure to optimize predictions, based on the data received. Hence, the first objective of this book is to shed light on key principles and methods that are common within both fields.
Machine learning and optimization share three components: representation, evaluation and iterative search. Yet while optimization solvers are generally designed to be fast and accurate on implicit models, machine learning methods need to be generic and trained offline on datasets. Machine learning problems present new challenges for optimization researchers, and machine learning practitioners seek simpler, generic optimization algorithms.
Quite recently, modern approaches to machine learning have also been applied to the design of optimization algorithms themselves, taking advantage of their ability to capture valuable information from complex structures in large spaces. Those capacities appear to be useful, especially for the representation and evaluation components. As large, complex structures are ubiquitous in optimization problems, and can be used as huge implicit datasets, the use of machine learning enabled the efficiency and genericity of optimization methods to be improved.
This book presents modern advances in the selection, configuration and engineering of algorithms that rely on machine learning and optimization. It is structured into two parts. Part 1 is dedicated to the most common optimization applications. Part 2 describes and implements several applications of machine learning.
Part 1 comprises four chapters which focus on real-world application of optimization algorithms.
Chapter 1 addresses the problem of vehicle routing with loading constraints and combines two combinatorial optimization problems: the capacity vehicle routing problem (CVRP) and the two-/three-dimensional bin packing problem (2/3D-BPP). The authors have studied real transport problems such as the transport of furniture or industrial machinery.
The main objective of Chapter 2 is to create the most appropriate scheduling solution that optimizes several QoS metrics simultaneously; thus, the authors adapt the widely used metaheuristic, “Genetic Algorithm” as an optimization method. The proposed scheduling approach is tested by simulating a healthcare IoT application, modeled as a workflow and several scientific workflow benchmarks. The results show the effectiveness of the proposed approach; it generates a scheduling plan that better optimizes the various QoS metrics considered.
Chapter 3 focuses on the grey wolf optimization (GWO) and its adaptation to a continuous search space. It begins by addressing the mathematical modeling of optimization in a binary discrete search space. Binarization modules are then provided, allowing continuous metaheuristics for the solution of feature selection problems in a binary search space. These binarization modules are then used to create the binary metaheuristic bGWO. Finally, an experimental demonstration shows the performance of bGWO in solving feature selection problems on 18 datasets from the UCI Machine Learning Repository
Chapter 4 addresses the type-2 mixed-model assembly line balancing problem with deterministic task times. To solve this problem, an enhancement of the greedy randomized adaptive search procedure – known as the reactive greedy randomized adaptive search procedure – is proposed. This reactive version is based on variation of the restricted candidate list parameter value, alpha. The proposed reactive GRASP is hybridized with the ranked positional weight heuristic to construct initial solutions. Results obtained by the proposed hybrid reactive GRASP are compared with those obtained by the basic GRASP, demonstrating the effect of the learning mechanism.
Part 2 comprises four chapters devoted to artificial intelligence and machine learning and their applications.
The main challenge of recommender systems comes from modeling the dependence between the various entities, incorporating multifaceted information such as user preferences, item attributes and users’ mutual influence, which results in more complex features. To deal with this issue, the authors of Chapter 5 design stacked ensemble machine learning models for recommendations. Their recommender system incorporates a collaborative filtering (CF) module and a stacking recommender module. An interactive attention mechanism is then introduced to model the mutual influence relationship between aspect users and items. Experiments on real-world datasets demonstrate that the proposed algorithm can achieve more accurate predictions and higher recommendation efficiency.
In internal auditing, the ability to process all of the available information related to the audit universe or subject could improve the quality of results. Classifying the audit text documents (unstructured data) could enable the use of additional information to improve the existing structured data, creating better knowledge support for the audit process. A comparison of results of classical machine learning and deep learning algorithms, combined with advanced word embeddings to classify the findings of internal audit reports, is presented in Chapter 6.
The design of a control architecture is a central problem in a project to realize an autonomous mobile robot. In the absence of a generic solution, it is essential to come up with a new approach detailing the design process of an intelligent system that is capable of adapting to all changes in the navigation environment. Chapter 7 proposes to use the multiagent paradigm and fuzzy logic in the design of the control architecture for the autonomous navigation of the mobile robot in a constrained environment. The control architecture is designed to solve various problems created during navigation. It is made up of four agents: the perception of the agent, the feasibility of the agent, the locomotion agent and the fuzzy control agent.
Intrusion detection is a key concept in modern computer network security. It is aimed at analyzing the current state of a network in real time and identifying potential anomalies in the system, reporting them as soon as they are identified. This allows for the detection of previously unknown malware. Artificial neural networks are supervised machine learning algorithms inspired by the human brain. This kind of network is a popular choice among data mining techniques today and has already been proven to be a valuable choice for intrusion detection. In Chapter 8, the author builds a feed-forward neural network trained on the NSL-KDD dataset, in order to classify network connections as belonging to one of two possible categories: normal or anomalous. Its goal is to maximize the level of accuracy in recognizing new data samples.
