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Optimization and Machine Learning
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Optimization and Machine Learning
by Rachid Chelouah, Patrick Siarry
Optimization and Machine Learning
Cover
Title Page
Copyright
Introduction
PART 1 Optimization
PART 2 Machine Learning
List of Authors
Index
End User License Agreement
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Title Page
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
List of Illustrations
Chapter 1
Figure 1.1.
An example of a 2L-CVRP solution
Figure 1.2.
An example of a 3L-CVRP solution
Chapter 2
Figure 2.1. Solution architecture. For a color version of this figure, see www.i...
Figure 2.2.
GA flowchart
Figure 2.3.
Workflow scheduling process
Figure 2.4. Workflow example. For a color version of this figure, see www.iste.c...
Figure 2.5. Example of resources. For a color version of this figure, see www.is...
Figure 2.6.
Illustration of crossover operator
Figure 2.7.
Illustration of mutation operation
Figure 2.8.
Health monitoring process
Figure 2.9. Inspiral-15 workflow benchmark. For a color version of this figure, ...
Figure 2.10. Percentage of IoT workflow executed tasks on Fog computing and Clou...
Figure 2.11. Percentage of Inspiral-15 workflow executed tasks on Fog computing ...
Figure 2.12. Average of latency when executing the healthcare workflow and Inspi...
Figure 2.13. Average of makespan when executing the healthcare workflow and Insp...
Figure 2.14. Average of cost when executing the healthcare workflow and Inspiral...
Chapter 3
Figure 3.1.
Wolf pack hierarchy
Figure 3.2.
Search, pursue, surround-harass and attack the prey
Figure 3.3.
Pack hierarchy modeling
Figure 3.4.
Alpha, Beta, Delta and Omega Wolf positions around prey
Figure 3.5. Position of the wolf agent and its prey in a square with N = 2 (Mirj...
Figure 3.6. Position of the wolf agent and its prey in a cube with N = 3 (Mirjal...
Figure 3.7. Steering of exploration and exploitation phases with vector A during...
Figure 3.8.
Search exploration phase
|
A
|≥
1 and attack exploitation phase 1
>|
A
|≥0...
Figure 3.9. Feature selection process. For a color version of this figure, see w...
Figure 3.10.
Binary search space with N=3
Figure 3.11.
Selection vector of features D2 and D5
Figure 3.12.
Border of optimal dominant solutions with the Pareto meaning
Figure 3.13. S and V transfer function (Mirjalili and Lewis 2013). For a color v...
Chapter 4
Figure 4.1.
Assembly line balancing problem categories
Figure 4.2. Mixed- and multi-model assembly lines. For a color version of this f...
Figure 4.3.
Precedence relations
Figure 4.4.
Neighborhood search procedure for the ALBP
Figure 4.5. Variation of probabilities during solving problem 01. For a color ve...
Figure 4.6. Variation of probabilities during solving problem 02. For a color ve...
Figure 4.7. Variation of probabilities during solving problem 03. For a color ve...
Chapter 5
Figure 5.1. The Interactive Personalized Recommender framework models the mutual...
Figure 5.2. The Interactive Personalized Recommender framework models the mutual...
Figure 5.3.
The interactive attention network recommender
Figure 5.4.
The stacked content-based filtering recommender
Figure 5.5.
Hyperparameter searching for the stack-based random forest
Figure 5.6. Hyperparameters analysis for interactive attention network recommend...
Figure 5.7. Visualization of the interactive co-attention weights for 10 items a...
Figure 5.8. Results of the comparison on MovieLens dataset. Evaluation of the pe...
Figure 5.9. Results of the comparison on MovieLens dataset. Evaluation of the pe...
Chapter 6
Figure 6.1.
Text classification steps and methods tested
Chapter 7
Figure 7.1.
Mobile robot navigation approaches
Figure 7.2.
Structure of robot architecture
Figure 7.3. Mobile robot control architecture based agent. For a color version o...
Figure 7.4. Measurements provided by infrared sensors (information of perception...
Figure 7.5. Method for calculating speeds of the right and left wheels of the ro...
Figure 7.6.
Process of locomotion agent
Figure 7.7.
Feasibility agent general process
Figure 7.8.
Fuzzy controller agent general process
Figure 7.9. Agent interaction and communication. For a color version of this fig...
Figure 7.10.
Presentation of the robot
Figure 7.11.
Robot and obstacles information
Figure 7.12.
Fuzzy controller agent interaction with navigation system agent
Figure 7.13.
Representation of fuzzy subsets of the distance
Figure 7.14.
Representation of fuzzy subsets of the γ
Figure 7.15.
Representation of output fuzzy subsets Δθ
Figure 7.16.
Representation of output fuzzy subsets Δv
Figure 7.17.
Representation of the eight different situations listed
Figure 7.18.
Situation 1 (avoid near in front of)
Figure 7.19.
Situation 2 (avoid near left)
Figure 7.20.
Situation 3 (avoid near right)
Figure 7.21.
Situation 4 (situation corridor)
Figure 7.22.
Situation 5 (blocking)
Figure 7.23.
Situation 6 (corner left)
Figures 7.24.
Situation 7 (corner right)
Figure 7.25.
Situation 8 (far obstacle: approaching the goal)
Figure 7.26. The robot, in environments 1 and 2, reaches the target (all obstacl...
Figure 7.27. The robot reaches the target in complex environments (environments ...
Figure 7.28. The robot in a deadlock situation. For a color version of this figu...
Chapter 8
Figure 8.1.
Land category distribution
Figure 8.2.
Root_shell category distribution
Figure 8.3.
Logged_in category distribution
Figure 8.4.
Is_host_login category distribution
Figure 8.5.
Is_guest_login category distribution
Figure 8.6.
Distribution of the target column values in the train set
Figure 8.7.
Distribution of the target column values in the test set
Figure 8.8.
Protocol type value distribution
Figure 8.9.
Flag value distribution
Figure 8.10.
Su attempted value distribution
Figure 8.11.
Wrong fragment value distribution
Figure 8.12.
Urgent value distribution
Figure 8.13.
Service value distribution
Figure 8.14. Distribution of discrete and continuous features inside the normali...
Figure 8.15. Correlation matrix of all the features of the dataset. A lighter co...
Figure 8.16. Distribution of the values of each numerical feature in the normali...
Figure 8.17. Distribution of the values of each numerical feature in the normali...
Figure 8.18.
Distribution of src_bytes before normalization
Figure 8.19.
Distribution of src_bytes after normalization
Figure 8.20.
Distribution of dst_host_count before normalization
Figure 8.21.
Distribution of dst_host_count after normalization
Figure 8.22. Top features ranked by importance found by the ExtraTrees Classifie...
Figure 8.23.
Top 30 features ranked by importance found by univariate selection
Figure 8.24. Learning curves (accuracy and loss) of the first model (all feature...
Figure 8.25. Learning curves (accuracy and loss) of the second model (all featur...
Figure 8.26. Learning curves (accuracy and loss) of the third model (all feature...
Figure 8.27.
Learning curves (accuracy and loss) of the low dropout model
Figure 8.28.
Possible points of interception for an NIDS
Figure 8.29.
Possible steps of the IDS once deployed in a real system
Figure 8.30.
Components of the TFX framework as described in the official guide
List of Tables
Chapter 1
Table 1.1.
Comparative study of the 2L-CVRP
Table 1.2.
Comparative study of the 3L-CVRP
Chapter 2
Table 2.1.
Comparison of existing scheduling approaches
Table 2.2.
Solution encoding
Chapter 3
Table 3.1. Most commonly used S and V transfer functions for feature section (Mi...
Table 3.2.
Wrapper parameters
Table 3.3.
Wrapper performance metrics
Chapter 4
Table 4.1. The classification of simple assembly line balancing problems with th...
Table 4.2. The classification of mixed-model assembly line balancing problems wi...
Table 4.3.
Ranked positional weight list (RPW_list)
Table 4.4.
Iteration x (α = 1)
Table 4.5.
Iteration y (α = 0.5)
Table 4.6.
Iteration z (α = 0.8)
Table 4.7.
Comparison of solutions
Table 4.8.
Small-sized problem
Table 4.9.
Medium-sized problem
Table 4.10.
Large-sized problem
Table 4.11.
Parameters used in solving proposed problems
Table 4.12. Results obtained by hybrid reactive GRASP and hybrid basic GRASP aft...
Table 4.13. Results obtained by hybrid reactive GRASP and hybrid basic GRASP aft...
Table 4.14. Results obtained by hybrid reactive GRASP and basic GRASP after solv...
Table 4.15. General comparison between the hybrid reactive GRASP and the basic G...
Chapter 5
Table 5.1.
Notations
Table 5.2.
MovieLens 1M specifications
Table 5.3.
The best scoring stacked content-based filtering recommenders
Table 5.4. Recommendation accuracy scores (%) of compared methods conducted on M...
Chapter 6
Table 6.1. Results for composition Extract Features and ML algorithms – Simple E...
Table 6.2. Results for composition Extract Features and ML algorithms – Simple E...
Table 6.3. Results for composition Extract Features and ML algorithms – FastText...
Table 6.4. Results for composition Extract Features and ML algorithms – FastText...
Table 6.5.
Results for composition Extract Features and ML algorithms – BERT
Table 6.6.
Confusion matrix
Chapter 8
Table 8.1.
Summary of the attributes of the NSL-KDD dataset
Table 8.2.
Features of the NSL-KDD dataset, divided by type
Table 8.3.
Shape and results of the first model
Table 8.4.
Shape and results of the second and third models
Table 8.5.
Shape and results of the dropout models
Table 8.6.
Shape and results of the deep models
Table 8.7. Comparison between models trained on ExtraTrees Classifier selected f...
Guide
Cover
Table of Contents
Title Page
Copyright
Introduction
Begin Reading
List of Authors
Index
End User License Agreement
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