Genetic algorithms and evolutionary computation, 2.
CONTENTS NOTE
Text of Note
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.
SUMMARY OR ABSTRACT
Text of Note
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is devoted to a new paradigm for evolutionary computation, named estimation of distribution algorithms (EDAs).