Modern Optimization with R [electronic resource] / by Paulo Cortez.

By:

Cortez, Paulo [author.]

Contributor(s):

SpringerLink (Online service)

Material type: Text

TextSeries: Use R!Publisher: Cham : Springer International Publishing : Imprint: Springer, 2014Description: XIII, 188 p. 33 illus. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783319082639

Subject(s):

Additional physical formats: Printed edition:: No titleDDC classification:

519.6 23

LOC classification:

QA402.5-402.6

Online resources:

Click here to access online

Contents:

1. Introduction -- 2. R Basics -- 3. Blind Search -- 4. Local Search -- 5. Population-Based Search -- 6. Multi-Objective Optimization -- 7. Applications.

In: Springer eBooksSummary: 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.

Holdings ( 1 )
Title notes ( 2 )

Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
eBook	e-Library	EBook		Available

Total holds: 0

1. Introduction -- 2. R Basics -- 3. Blind Search -- 4. Local Search -- 5. Population-Based Search -- 6. Multi-Objective Optimization -- 7. Applications.

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.