Ebook: Markov Networks in Evolutionary Computation

Markov networks and other probabilistic graphical modes have recently received an upsurge in attention from Evolutionary computation community, particularly in the area of Estimation of distribution algorithms (EDAs). EDAs have arisen as one of the most successful experiences in the application of machine learning methods in optimization, mainly due to their efficiency to solve complex real-world optimization problems and their suitability for theoretical analysis.

This book focuses on the different steps involved in the conception, implementation and application of EDAs that use Markov networks, and undirected models in general. It can serve as a general introduction to EDAs but covers also an important current void in the study of these algorithms by explaining the specificities and benefits of modeling optimization problems by means of undirected probabilistic models.

All major developments to date in the progressive introduction of Markov networks based EDAs are reviewed in the book. Hot current research trends and future perspectives in the enhancement and applicability of EDAs are also covered. The contributions included in the book address topics as relevant as the application of probabilistic-based fitness models, the use of belief propagation algorithms in EDAs and the application of Markov network based EDAs to real-world optimization problems. The book should be of interest to researchers and practitioners from areas such as optimization, evolutionary computation, and machine learning.

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Markov networks and other probabilistic graphical modes have recently received an upsurge in attention from Evolutionary computation community, particularly in the area of Estimation of distribution algorithms (EDAs). EDAs have arisen as one of the most successful experiences in the application of machine learning methods in optimization, mainly due to their efficiency to solve complex real-world optimization problems and their suitability for theoretical analysis.

This book focuses on the different steps involved in the conception, implementation and application of EDAs that use Markov networks, and undirected models in general. It can serve as a general introduction to EDAs but covers also an important current void in the study of these algorithms by explaining the specificities and benefits of modeling optimization problems by means of undirected probabilistic models.

All major developments to date in the progressive introduction of Markov networks based EDAs are reviewed in the book. Hot current research trends and future perspectives in the enhancement and applicability of EDAs are also covered. The contributions included in the book address topics as relevant as the application of probabilistic-based fitness models, the use of belief propagation algorithms in EDAs and the application of Markov network based EDAs to real-world optimization problems. The book should be of interest to researchers and practitioners from areas such as optimization, evolutionary computation, and machine learning.

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Markov networks and other probabilistic graphical modes have recently received an upsurge in attention from Evolutionary computation community, particularly in the area of Estimation of distribution algorithms (EDAs). EDAs have arisen as one of the most successful experiences in the application of machine learning methods in optimization, mainly due to their efficiency to solve complex real-world optimization problems and their suitability for theoretical analysis.

This book focuses on the different steps involved in the conception, implementation and application of EDAs that use Markov networks, and undirected models in general. It can serve as a general introduction to EDAs but covers also an important current void in the study of these algorithms by explaining the specificities and benefits of modeling optimization problems by means of undirected probabilistic models.

All major developments to date in the progressive introduction of Markov networks based EDAs are reviewed in the book. Hot current research trends and future perspectives in the enhancement and applicability of EDAs are also covered. The contributions included in the book address topics as relevant as the application of probabilistic-based fitness models, the use of belief propagation algorithms in EDAs and the application of Markov network based EDAs to real-world optimization problems. The book should be of interest to researchers and practitioners from areas such as optimization, evolutionary computation, and machine learning.

Content:
Front Matter....Pages 1-17
Front Matter....Pages 1-1
Probabilistic Graphical Models and Markov Networks....Pages 3-19
A Review of Estimation of Distribution Algorithms and Markov Networks....Pages 21-37
MOA - Markovian Optimisation Algorithm....Pages 39-53
DEUM - Distribution Estimation Using Markov Networks....Pages 55-71
MN-EDA and the Use of Clique-Based Factorisations in EDAs....Pages 73-87
Front Matter....Pages 89-89
Convergence Theorems of Estimation of Distribution Algorithms....Pages 91-108
Adaptive Evolutionary Algorithm Based on a Cliqued Gibbs Sampling over Graphical Markov Model Structure....Pages 109-123
The Markov Network Fitness Model....Pages 125-140
Fast Fitness Improvements in Estimation of Distribution Algorithms Using Belief Propagation....Pages 141-155
Continuous Estimation of Distribution Algorithms Based on Factorized Gaussian Markov Networks....Pages 157-173
Using Maximum Entropy and Generalized Belief Propagation in Estimation of Distribution Algorithms....Pages 175-190
Front Matter....Pages 191-191
Applications of Distribution Estimation Using Markov Network Modelling (DEUM)....Pages 193-207
Vine Estimation of Distribution Algorithms with Application to Molecular Docking....Pages 209-225
EDA-RL: EDA with Conditional Random Fields for Solving Reinforcement Learning Problems....Pages 227-239
Back Matter....Pages 0--1

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Markov networks and other probabilistic graphical modes have recently received an upsurge in attention from Evolutionary computation community, particularly in the area of Estimation of distribution algorithms (EDAs). EDAs have arisen as one of the most successful experiences in the application of machine learning methods in optimization, mainly due to their efficiency to solve complex real-world optimization problems and their suitability for theoretical analysis.

This book focuses on the different steps involved in the conception, implementation and application of EDAs that use Markov networks, and undirected models in general. It can serve as a general introduction to EDAs but covers also an important current void in the study of these algorithms by explaining the specificities and benefits of modeling optimization problems by means of undirected probabilistic models.

All major developments to date in the progressive introduction of Markov networks based EDAs are reviewed in the book. Hot current research trends and future perspectives in the enhancement and applicability of EDAs are also covered. The contributions included in the book address topics as relevant as the application of probabilistic-based fitness models, the use of belief propagation algorithms in EDAs and the application of Markov network based EDAs to real-world optimization problems. The book should be of interest to researchers and practitioners from areas such as optimization, evolutionary computation, and machine learning.

Content:
Front Matter....Pages 1-17
Front Matter....Pages 1-1
Probabilistic Graphical Models and Markov Networks....Pages 3-19
A Review of Estimation of Distribution Algorithms and Markov Networks....Pages 21-37
MOA - Markovian Optimisation Algorithm....Pages 39-53
DEUM - Distribution Estimation Using Markov Networks....Pages 55-71
MN-EDA and the Use of Clique-Based Factorisations in EDAs....Pages 73-87
Front Matter....Pages 89-89
Convergence Theorems of Estimation of Distribution Algorithms....Pages 91-108
Adaptive Evolutionary Algorithm Based on a Cliqued Gibbs Sampling over Graphical Markov Model Structure....Pages 109-123
The Markov Network Fitness Model....Pages 125-140
Fast Fitness Improvements in Estimation of Distribution Algorithms Using Belief Propagation....Pages 141-155
Continuous Estimation of Distribution Algorithms Based on Factorized Gaussian Markov Networks....Pages 157-173
Using Maximum Entropy and Generalized Belief Propagation in Estimation of Distribution Algorithms....Pages 175-190
Front Matter....Pages 191-191
Applications of Distribution Estimation Using Markov Network Modelling (DEUM)....Pages 193-207
Vine Estimation of Distribution Algorithms with Application to Molecular Docking....Pages 209-225
EDA-RL: EDA with Conditional Random Fields for Solving Reinforcement Learning Problems....Pages 227-239
Back Matter....Pages 0--1
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