Evolutionary Algorithms in Molecular Design

Evolutionary Algorithms in Molecular Design  

Posted by DZ123 at Dec. 13, 2014
Evolutionary Algorithms in Molecular Design

David E. Clark, Raimund Mannhold, Hugo Kubinyi, Hendrik Timmerman, "Evolutionary Algorithms in Molecular Design"
English | 2000 | ISBN: 3527301550 | PDF | pages: 284 | 18,9 mb
Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Proceedings of ICAEES 2014, Volume 1

L Padma Suresh, Subhransu Sekhar Dash, Bijaya Ketan Panigrahi - Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Proceedings of ICAEES 2014, Volume 1
Published: 2014-11-01 | ISBN: 8132221257 | PDF | 862 pages | 20.38 MB
Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Proceedings of ICAEES 2014, Volume 2 [Repost]‎

L Padma Suresh, Subhransu Sekhar Dash, Bijaya Ketan Panigrahi - Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Proceedings of ICAEES 2014, Volume 2‎
Published: 2014-11-25 | ISBN: 8132221346 | PDF | 873 pages | 28 MB
Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Proceedings of ICAEES 2014, Volume 2

Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Proceedings of ICAEES 2014, Volume 2 (Advances in Intelligent Systems and Computing, Book 325) by L Padma Suresh and Subhransu Sekhar Dash
English | 2014 | ISBN: 8132221346 | 873 pages | PDF | 28,7 MB
Chemometric Methods in Molecular Design (Methods and Principles in Medicinal Chemistry) by Han Waterbeemd

Chemometric Methods in Molecular Design (Methods and Principles in Medicinal Chemistry) by Han Waterbeemd
English | February 1995 | ISBN: 3527300449 | 361 pages | PDF | 17 MB

The statistical analysis of experimental and theoretical data lies at the heart of modern drug design. This practice-oriented handbook is a comprehensive account of modern chemometric methods in molecular design. It presents strategies for making more rational choices in the planning of syntheses, and describes techniques for analyzing biological and chemical data.

Applied Evolutionary Algorithms in Java  

Posted by arundhati at Jan. 13, 2014
Applied Evolutionary Algorithms in Java

Robert Ghanea-Hercock, "Applied Evolutionary Algorithms in Java"
2003 | ISBN-10: 1468495267, 0387955682 | 219 pages | PDF | 5,7 MB
Evolutionary Algorithms in Engineering and Computer Science (Repost)

Evolutionary Algorithms in Engineering and Computer Science: Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming and Industrial Applications By K. Miettinen, Pekka Neittaanmäki, M. M. Mäkelä, Jacques Périaux
1999 | 500 Pages | ISBN: 0471999024 | PDF | 28 MB
Evolutionary Algorithms in Engineering and Computer Science

Evolutionary Algorithms in Engineering and Computer Science: Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming and Industrial Applications By K. Miettinen, Pekka Neittaanmäki, M. M. Mäkelä, Jacques Périaux
Publisher: Wiley 1999 | 500 Pages | ISBN: 0471999024 | PDF | 22 MB
Thomas B: Evolutionary Algorithms in Theory and Practice

Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms
Oxford University Press, USA | 1996-01-11 | ISBN 0195099710 | DJVU | Pages 328 | 5.31 Mb
Evolutionary Algorithms in Theory and Practice : Evolution Strategies, Evolutionary Programming, Genetic Algorithms

Evolutionary Algorithms in Theory and Practice : Evolution Strategies, Evolutionary Programming, Genetic Algorithms
328 pages| Oxford University Press USA | ISBN: 0195099710

“This book presents a unified view of evolutionary algorithms: the exciting new probabilistic search tools inspired by biological models that have immense potential as practical problem-solvers in a wide variety of settings, academic, commercial, and industrial. In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming. The algorithms are presented within a unified framework, thereby clarifying the similarities and differences of these methods. The author also presents new results regarding the role of mutation and selection in genetic algorithms, showing how mutation seems to be much more important for the performance of genetic algorithms than usually assumed. The interaction of selection and mutation, and the impact of the binary code are further topics of interest. Some of the theoretical results are also confirmed by performing an experiment in meta-evolution on a parallel computer. The meta-algorithm used in this experiment combines components from evolution strategies and genetic algorithms to yield a hybrid capable of handling mixed integer optimization problems. As a detailed description of the algorithms, with practical guidelines for usage and implementation, this work will interest a wide range of researchers in computer science and engineering disciplines, as well as graduate students in these fields”.