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Peasantry --- Land reform --- Social conflict --- History --- Peasants --- Class conflict --- Class struggle --- Conflict, Social --- Social tensions --- Interpersonal conflict --- Social psychology --- Sociology --- Agricultural laborers --- Rural population --- Marks (Medieval land tenure) --- Villeinage --- Agrarian reform --- Economic policy --- Land use, Rural --- Social policy --- Agriculture and state --- Peasants - Chile - History - 20th century --- Land reform - Chile - History - 20th century --- Social conflict - Chile - History - 20th century

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IDENTITE CULTURELLE --- STRATEGIES PATRIMONIALES --- ARCHITECTURE EN BOIS --- REVITALISATION ARCHITECTURALE --- DEVELOPPEMENT LOCAL --- CHILI

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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.

Artificial intelligence. --- Evolutionary computation. --- Markov processes. --- Markov processes --- Evolutionary computation --- Engineering & Applied Sciences --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Computer Science --- Economics, Mathematical. --- Economics --- Mathematical economics --- Computation, Evolutionary --- Analysis, Markov --- Chains, Markov --- Markoff processes --- Markov analysis --- Markov chains --- Markov models --- Models, Markov --- Processes, Markov --- Engineering. --- Computational intelligence. --- Evolutionary economics. --- Computational Intelligence. --- Artificial Intelligence (incl. Robotics). --- Institutional/Evolutionary Economics. --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Construction --- Industrial arts --- Technology --- Econometrics --- Neural networks (Computer science) --- Stochastic processes --- Methodology --- Artificial Intelligence.

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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.

Operational research. Game theory --- Artificial intelligence. Robotics. Simulation. Graphics --- neuronale netwerken --- fuzzy logic --- cybernetica --- speltheorie --- KI (kunstmatige intelligentie) --- robots