Table of Contents

I Foundations.- 1 An Introduction to Evolutionary Algorithms.- 2 An Introduction to Probabilistic Graphical Models.- 3 A Review on Estimation of Distribution Algorithms.- 4 Benefits of Data Clustering in Multimodal Function Optimization via EDAs.- 5 Parallel Estimation of Distribution Algorithms.- 6 Mathematical Modeling of Discrete Estimation of Distribution Algorithms.- II Optimization.- 7 An Empirical Comparison of Discrete Estimation of Distribution Algorithms.- 8 Results in Function Optimization with EDAs in Continuous Domain.- 9 Solving the 0-1 Knapsack Problem with EDAs.- 10 Solving the Traveling Salesman Problem with EDAs.- 11 EDAs Applied to the Job Shop Scheduling Problem.- 12 Solving Graph Matching with EDAs Using a Permutation-Based Representation.- III Machine Learning.- 13 Feature Subset Selection by Estimation of Distribution Algorithms.- 14 Feature Weighting for Nearest Neighbor by EDAs.- 15 Rule Induction by Estimation of Distribution Algorithms.- 16 Partial Abductive Inference in Bayesian Networks: An Empirical Comparison Between GAs and EDAs.- 17 Comparing K-Means, GAs and EDAs in Partitional Clustering.- 18 Adjusting Weights in Artificial Neural Networks using Evolutionary Algorithms.

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