| 书目名称 | Markov Networks in Evolutionary Computation |
| 编辑 | Siddhartha Shakya,Roberto Santana |
| 视频video | |
| 概述 | Offers a systematic presentation of the use of Markov Networks in Evolutionary Computation.Fills a void in the current literature on the application of PGMs in evolutionary optimization.Written by lea |
| 丛书名称 | Adaptation, Learning, and Optimization |
| 图书封面 |  |
| 描述 | .Markov networks and other probabilistic graphical modes have recently received an upsurge in attention from Evolutionary computation community, particularly in the area of Estimation of distribution algorithms (EDAs). EDAs have arisen as one of the most successful experiences in the application of machine learning methods in optimization, mainly due to their efficiency to solve complex real-world optimization problems and their suitability for theoretical analysis..This book focuses on the different steps involved in the conception, implementation and application of EDAs that use Markov networks, and undirected models in general. It can serve as a general introduction to EDAs but covers also an important current void in the study of these algorithms by explaining the specificities and benefits of modeling optimization problems by means of undirected probabilistic models. .All major developments to date in the progressive introduction of Markov networks based EDAs are reviewed in the book. Hot current research trends and future perspectives in the enhancement and applicability of EDAs are also covered. The contributions included in the book address topics as relevant as the appli |
| 出版日期 | Book 2012 |
| 关键词 | Estimation of Distribution Algorithms; Evolutionary Algorithms; Graphical Models; Markov Models; Metaheu |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-642-28900-2 |
| isbn_softcover | 978-3-642-44494-4 |
| isbn_ebook | 978-3-642-28900-2Series ISSN 1867-4534 Series E-ISSN 1867-4542 |
| issn_series | 1867-4534 |
| copyright | Springer Berlin Heidelberg 2012 |
发表于 2025-3-21 18:47:02
