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＜電子ブック＞
Modern Optimization with R / by Paulo Cortez
(Use R!. ISSN:21975744)

版	2nd ed. 2021.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2021
大きさ	XVII, 254 p. 43 illus : online resource
著者標目	*Cortez, Paulo author SpringerLink (Online service)
件　名	LCSH:Mathematical statistics—Data processing LCSH:Mathematical optimization LCSH:Artificial intelligence—Data processing LCSH:Sampling (Statistics) LCSH:Artificial intelligence LCSH:Programming languages (Electronic computers) FREE:Statistics and Computing FREE:Optimization FREE:Data Science FREE:Methodology of Data Collection and Processing FREE:Artificial Intelligence FREE:Programming Language
一般注記	Chapter 1. introduction -- Chapter 2. R. Basics -- Chapter 3. Blind Search -- Chapter 4. Local Search -- Chapter 5. Population Based Search -- Chapter 6. Multi-Object Optimization The goal of this book is to gather in a single document the most relevant concepts related to modern optimization methods, showing how such concepts and methods can be addressed using the open source, multi-platform R tool. Modern optimization methods, also known as metaheuristics, are particularly useful for solving complex problems for which no specialized optimization algorithm has been developed. These methods often yield high quality solutions with a more reasonable use of computational resources (e.g. memory and processing effort). Examples of popular modern methods discussed in this book are: simulated annealing; tabu search; genetic algorithms; differential evolution; and particle swarm optimization. This book is suitable for undergraduate and graduate students in Computer Science, Information Technology, and related areas, as well as data analysts interested in exploring modern optimization methods using R. This new edition integrates the latest R packages through text and code examples. It also discusses new topics, such as: the impact of artificial intelligence and business analytics in modern optimization tasks; the creation of interactive Web applications; usage of parallel computing; and more modern optimization algorithms (e.g., iterated racing, ant colony optimization, grammatical evolution). HTTP:URL=https://doi.org/10.1007/978-3-030-72819-9

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