Scalable Optimization via Probabilistic Modeling - Info and Reading Options
From Algorithms to Applications (Studies in Computational Intelligence)
By Martin Pelikan and Kumara Sastry
"Scalable Optimization via Probabilistic Modeling" is published by Springer in November 14, 2006, it has 350 pages and the language of the book is English.
“Scalable Optimization via Probabilistic Modeling” Metadata:
- Title: ➤ Scalable Optimization via Probabilistic Modeling
- Authors: Martin PelikanKumara Sastry
- Language: English
- Number of Pages: 350
- Publisher: Springer
- Publish Date: November 14, 2006
“Scalable Optimization via Probabilistic Modeling” Subjects and Themes:
- Subjects: ➤ Genetic algorithms - Apprentissage automatique - Data processing - Distribution (Théorie des probabilités) - Combinatorial optimization - Distribution (Probability theory) - Machine learning - Evolutionary computation - Optimisation combinatoire - Logiciels - Réseaux neuronaux à structure évolutive - Algorithmes génétiques - Engineering - Engineering mathematics - Artificial intelligence - Distribution (probability theory)
Edition Specifications:
- Format: Hardcover
- Weight: 1.5 pounds
- Dimensions: 9.2 x 6.3 x 1 inches
Edition Identifiers:
- The Open Library ID: OL9056602M - OL16947976W
- Library of Congress Control Number (LCCN): 2006928618
- ISBN-13: 9783540349532
- ISBN-10: 3540349537
- All ISBNs: 3540349537 - 9783540349532
AI-generated Review of “Scalable Optimization via Probabilistic Modeling”:
Snippets and Summary:
Estimation of distribution algorithms (EDAs) [1, 5, 8, 11] address broad classes of optimization problems by learning explicit probabilistic models of promising solutions found so far and sampling the built models to generate new candidate solutions.
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