Posted by **roxul** at May 25, 2017

2015 | ISBN-10: 1107019656 | 260 pages | true PDF | 3 MB

Posted by **nebulae** at April 21, 2017

English | ISBN: 1848218044 | 2017 | 256 pages | PDF | 4 MB

Posted by **AlenMiler** at April 13, 2017

English | 11 Apr. 2017 | ASIN: B071XNSYYS | 256 Pages | AZW3 | 7.73 MB

Posted by **AlenMiler** at Dec. 29, 2015

English | Oct. 8, 2015 | ISBN: 1107019656 | 260 Pages | AZW3/PDF (conv) | 8.33 MB

What are the models used in phylogenetic analysis and what exactly is involved in Bayesian evolutionary analysis using Markov chain Monte Carlo (MCMC) methods?

Posted by **exLib** at Jan. 17, 2015

InTeOpP | 2008 | ISBN: 9537619117 9789537619114 | 4786 pages | PDF | 47 MB

Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge Evolutionary Algorithms field.

Posted by **fdts** at May 21, 2014

by Ernesto Sanchez

English | 2012 | ISBN: 3642274668 | 126 pages | PDF | 2.58 MB

Posted by **lout** at Oct. 23, 2010

Publisher: Wiley 1999 | 500 Pages | ISBN: 0471999024 | PDF | 22 MB

Posted by **srinivasgoud** at July 4, 2010

by Carlos A. Coello Coello, Gary B. Lamont, David A. Van Veldhuizen

Publisher: Springer | Publication Date:2007-09-18 | ISBN:0387332545 | 800 Pages | PDF | 9 MB

Solving multi-objective problems is an evolving effort, and computer science and other related disciplines have given rise to many powerful deterministic and stochastic techniques for addressing these large-dimensional optimization problems. Evolutionary algorithms are one such generic stochastic approach that has proven to be successful and widely applicable in solving both single-objective and multi-objective problems.

Posted by **misirac** at May 6, 2007

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…

Posted by **naag** at April 4, 2017

2012 | 469 Pages | ISBN: 3642234232 | PDF | 10 MB