Description: Scalable Optimization via Probabilistic Modeling by Martin Pelikan, Kumara Sastry, Erick CantÚ-Paz Estimated delivery 3-12 business days Format Paperback Condition Brand New Description The volume Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications is a worthy addition to your library because it succeeds on exactly those dimensions where so many edited volumes fail. Publisher Description 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. Im 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. David E Goldberg, University of Illinois at Urbana-Champaign This book is an excellent compilation of carefully selected topics in estimation of distribution algorithms---search algorithms that combine ideas from evolutionary algorithms and machine learning. The book covers a broad spectrum of important subjects ranging from design of robust and scalable optimization algorithms to efficiency enhancements and applications of these algorithms.The book should be of interest to theoreticians and practitioners alike, and is a must-have resource for those interested in stochastic optimization in general, and genetic and evolutionary algorithms in particular. John R. Koza, Stanford University This edited book portrays population-based optimization algorithms and applications, covering the entire gamut of optimization problems having single and multiple objectives, discrete and continuous variables, serial and parallel computations, and simple and complex function models. Anyone interested in population-based optimization methods, either knowingly or unknowingly, use some form of an estimation of distribution algorithm (EDA). This book is an eye-opener and a must-read text, covering easy-to-read yet erudite articles on established and emerging EDA methodologies from real experts in the field. Kalyanmoy Deb, Indian Institute of Technology Kanpur This book is an excellent comprehensive resource on estimation of distribution algorithms. It can serve as the primary EDA resource for practitioner or researcher.The book includes chapters from all major contributors to EDA state-of-the-art and covers the spectrum from EDA design to applications. These algorithms strategically combine the advantages of genetic and evolutionary computation with the advantages of statistical, model building machine learning techniques. EDAs are useful to solve classes of difficult real-world problems in a robust and scalable manner. Una-May OReilly, Massachusetts Institute of Technology Machine-learning methods continue to stir the publics imagination due to its futuristic implications. But, probability-based optimization methods can have great impact now on many scientific multiscale and engineering design problems, especially true with use of efficient and competent genetic algorithms (GA) which are the basis of the present volume. Even though efficient and competent GAs outperform standard techniques and prevent negative issues, such as solution stagnation, inherent in the older but more well-known GAs, they remain less known or embraced in the scientific and engineering communities.To that end, the editors have brought together a selection of experts that (1) introduce the current methodology and lexicography of the field with illustrative discussions and highly useful references, (2) exemplify these new techniques that dramatic improve performance in provable hard problems, and (3) provide real-world applications of these techniques, such as antenna design. As one who has strayed into the use of genetic algorithms and genetic programming for multiscale modeling in materials science, I can say it would have been personally more useful if this would have come out five years ago, but, for my students, it will be a boon. Duane D. Johnson, University of Illinois at Urbana-Champaign Details ISBN 3642071163 ISBN-13 9783642071164 Title Scalable Optimization via Probabilistic Modeling Author Martin Pelikan, Kumara Sastry, Erick CantÚ-Paz Format Paperback Year 2010 Pages 349 Edition 1st Publisher Springer-Verlag Berlin and Heidelberg GmbH & Co. KG GE_Item_ID:151350357; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items - you're bound to find what you want, at a price you'll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. Deliveries are made by either USPS or Courier. We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. NOTE: We are unable to offer combined shipping for multiple items purchased. This is because our items are shipped from different locations. Returns If you wish to return an item, please consult our Returns Policy as below: Please contact Customer Services and request "Return Authorisation" before you send your item back to us. Unauthorised returns will not be accepted. Returns must be postmarked within 4 business days of authorisation and must be in resellable condition. Returns are shipped at the customer's risk. We cannot take responsibility for items which are lost or damaged in transit. For purchases where a shipping charge was paid, there will be no refund of the original shipping charge. Additional Questions If you have any questions please feel free to Contact Us. Categories Baby Books Electronics Fashion Games Health & Beauty Home, Garden & Pets Movies Music Sports & Outdoors Toys
Price: 186.79 USD
Location: Fairfield, Ohio
End Time: 2024-11-25T03:26:07.000Z
Shipping Cost: 0 USD
Product Images
Item Specifics
Restocking Fee: No
Return shipping will be paid by: Buyer
All returns accepted: Returns Accepted
Item must be returned within: 30 Days
Refund will be given as: Money Back
ISBN-13: 9783642071164
Book Title: Scalable Optimization via Probabilistic Modeling
Number of Pages: Xx, 349 Pages
Publication Name: Scalable Optimization Via Probabilistic Modeling : from Algorithms to Applications
Language: English
Publisher: Springer Berlin / Heidelberg
Subject: Programming / Algorithms, Engineering (General), Probability & Statistics / General, Neural Networks, Combinatorics, Applied
Publication Year: 2010
Item Weight: 20.1 Oz
Type: Textbook
Item Length: 9.3 in
Subject Area: Mathematics, Computers, Technology & Engineering
Author: Kumara Sastry
Item Width: 6.1 in
Series: Studies in Computational Intelligence Ser.
Format: Trade Paperback
