Both installments of Steven Soderbergh's two-part 'Che' epic, a political drama based on the memoirs of Che Guevara starring Benicio del Toro as the iconic revolutionary. The first part follows events in late 1950s Cuba, when Che and a band of Cuban exiles led by Fidel Castro (Demian Bichir) set out for Cuba from Mexico and, over the next two years, mobilise an army in order to topple the US-supported dictatorship of Fulgenzio Batista. In the second part, Guevara moves on to foment revolutions in both Africa and South America, ending up in the mountains of Bolivia while being pursued by CIA-backed counter-insurgency raiders.

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.