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Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)

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By Jose A., Lozano, Pedro, Larranaga, Inaki, Inza,  

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Publisher:   Springer Verlag GmbH
Number Of Pages:   312
Publication Date:   2006-02-01
Sales Rank:   1453404
ISBN / ASIN:   3540290060
EAN:   9783540290063
Binding:   Hardcover
Manufacturer:   Springer Verlag GmbH
Studio:   Springer Verlag GmbH


Estimation of Distribution Algorithms (EDAs) are a set of algorithms in the Evolutionary Computation (EC) field characterized by the use of explicit probability distributions in optimization. Contrarily to other EC techniques such as the broadly known Genetic Algorithms (GAs) in EDAs, the crossover and mutation operators are substituted by the sampling of a distribution previously learnt from the selected individuals. EDAs have experienced a high development that has transformed them into an established discipline within the EC field.

This book attracts the interest of new researchers in the EC field as well as in other optimization disciplines, and that it becomes a reference for all of us working on this topic. The twelve chapters of this book can be divided into those that endeavor to set a sound theoretical basis for EDAs, those that broaden the methodology of EDAs and finally those that have an applied objective.

PDF, 6.31 MB, OCR

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Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms

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Martin Pelikan

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Springer | ISBN: 3540237747 | 2005. | 180 p. | RARed | PDF | 1.64MB

This book provides a framework for the design of competent optimization techniques by combining advanced evolutionary algorithms with state-of-the-art machine learning techniques. The primary focus of the book is on two algorithms that replace traditional variation operators of evolutionary algorithms, by learning and sampling Bayesian networks: the Bayesian optimization algorithm (BOA) and the hierarchical BOA (hBOA). They provide a scalable solution to a broad class of problems. The book provides an overview of evolutionary algorithms that use probabilistic models to guide their search, motivates and describes BOA and hBOA in a way accessible to a wide audience, and presents numerous results confirming that they are revolutionary approaches to black-box optimization...

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Advances in Evolutionary Computing for System Design (Studies in Computational Intelligence)

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Springer; 1 edition (August 24, 2007) | PDF(file-326pages) | 7.9mb(rar size)
Advances in Evolutionary Computing for System Design/byLakhmi C. Jain(Editor),Vasile Palade(Editor),Dipti Srinivasan(Editor)

Evolutionary computing paradigms offer robust and powerful adaptive search mechanisms for system design. This book includes thirteen chapters covering a wide area of topics in evolutionary computing and applications including: Introduction to evolutionary computing in system design; evolutionary neuro-fuzzy systems; evolution of fuzzy controllers; genetic algorithms for multi-classifier design; evolutionary grooming of traffic; evolutionary particle swarms; fuzzy logic systems using genetic algorithms; evolutionary algorithms and immune learning for neural network-based controller design; distributed problem solving using evolutionary learning; evolutionary computing within grid environment; evolutionary game theory in wireless mesh networks; hybrid multiobjective evolutionary algorithms for the sailor assignment problem; evolutionary techniques in hardware optimization. This book will be useful to researchers in intelligent systems with interest in evolutionary computing, application engineers and system designers. The book can also be used by students and lecturers as an advanced reading material for courses on evolutionary computing.


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