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Book Categories |
List of Figures | ||
List of Tables | ||
Preface | ||
Contributing Authors | ||
Series Foreword | ||
Pt. I | Foundations | |
1 | An Introduction to Evolutionary Algorithms | 3 |
2 | An Introduction to Probabilistic Graphical Models | 27 |
3 | A Review on Estimation of Distribution Algorithms | 57 |
4 | Benefits of Data Clustering in Multimodal Function Optimization via EDAs | 101 |
5 | Parallel Estimation of Distribution Algorithms | 129 |
6 | Mathematical Modeling of Discrete Estimation of Distribution Algorithms | 147 |
Pt. II | Optimization | |
7 | An Empirical Comparison of Discrete Estimation of Distribution Algorithms | 167 |
8 | Results in Function Optimization with EDAs in Continuous Domain | 181 |
9 | Solving the 0-1 Knapsack Problem with EDAs | 195 |
10 | Solving the Traveling Salesman Problem with EDAs | 211 |
11 | EDAs Applied to the Job Shop Scheduling Problem | 231 |
12 | Solving Graph Matching with EDAs Using a Permutation-Based Representation | 243 |
Pt. III | Machine Learning | |
13 | Feature Subset Selection by Estimation of Distribution Algorithms | 269 |
14 | Feature Weighting for Nearest Neighbor by EDAs | 295 |
15 | Rule Induction by Estimation of Distribution Algorithms | 313 |
16 | Partial Abductive Inference in Bayesian Networks: An Empirical Comparison Between GAs and EDAs | 323 |
17 | Comparing K-Means, GAs and EDAs in Partitional Clustering | 343 |
18 | Adjusting Weights in Artificial Neural Networks using Evolutionary Algorithms | 361 |
Index | 379 |
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