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Modern optimization with R

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Modern optimization with R

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modern optimization with R/ by Paulo Cortez.
Author:	Cortez, Paulo.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xvii, 254 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	R (Computer program language) -
Online resource:	https://link.springer.com/openurl.asp?genre=book&isbn=978-3-030-72819-9
ISBN:	9783030728199

Modern optimization with R
Cortez, Paulo.

Modern optimization with R[electronic resource] /by Paulo Cortez. - Second edition. - Cham :Springer International Publishing :2021. - xvii, 254 p. :ill., digital ;24 cm. - Use R!,2197-5736. - Use R!..

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)

ISBN: 9783030728199

Standard No.: 10.1007/978-3-030-72819-9doiSubjects--Topical Terms:

784593
R (Computer program language)

LC Class. No.: QA276.45.R3 / C67 2021

Dewey Class. No.: 519.50285

Modern optimization with R
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505 0 $a 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.
520 $a 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)
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