Home
Curriculum Vitae
Current Research
Publications
Books
Software
Presentations
Photos
Contact
MEDAL
MEDAL Blogging
BOA
hBOA
External links:
hBOATM
IlliGAL
ISGEC
EvoWeb
www.arxiv.org
www.one.org
more...
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications
Editors: Martin Pelikan, Kumara Sastry, Erick Cantu-Paz
List of authors: H. Abbass, U. Aickelin, S. Baluja, P. A. N. Bosman, M. Butz, E. Cantu-Paz, D. Essam, D. E. Goldberg, G. R. Harik, A. K. Hartmann, R. Hoe ns, J. Li, X. Llora, F. G. Lobo, R. I. McKay, H. Muehlenbein, J. Ocenasek, M. Pelikan, S. Santarelli, K. Sastry, J. Schwarz, Y. Shan, D. Thierens, T.-L. Yu
From the foreword by David E. Goldberg:
This book focuses like a laser beam on one of the hottest topics in evolutionary computation over the last decade or so: estimation of distribution algorithms (EDAs). EDAs are an important current technique that is leading to breakthroughs in genetic and evolutionary computation and in optimization more generally. I'm putting Scalable Optimization via Probabilistic Modeling in a prominent place in my library, and I urge you to do so as well. This volume summarizes the state of the art at the same time it points to where that art is going. Buy it, read it, and take its lessons to heart.
der here
Hierarchical Bayesian optimization algorithm: Toward a new generation of evolutionary algorithms
Martin Pelikan
Foreword by David E. Goldberg
About this book
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.
Order here
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2005)
Editors: Beyer, H.-G., O'Reilly, U. M., Arnold, D. V., Banzhaf, W., Blum, C., Bonabeau, E. W., Cantu-Paz, E., Dasgupta, D., Deb, K., Foster, J. A., de Jong, E. D., Lipson, H., Llora, X., Mancoridis, S., Pelikan, M., Raidl, G. R., Soule, T., Tyrrell, A. M., Watson, J.-P., Zitzler, E.
More information here
Last update: Fri Aug 1 05:57:03 CDT 2008 by Martin Pelikan