Book: Modern Optimization with R, Use R! series, Springer, 2014

The goal of this book is to gather in a single document the most relevant concepts related to modern optimization methods or metaheuristics (e.g. simulated annealing; tabu search; genetic algorithms; differential evolution; and particle swarm optimization), showing how such concepts and methods can be addressed using the open source R tool.

Table of contents

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

Downloads

• R code: Click here for all chapters R code
• Data: Some book examples retrieve data from the web. If needed, such data is available here.
• Exercise solution code: Click here for all solutions R code

How to Cite this book

Paulo Cortez, Modern Optimization with R, Use R! series, Springer, September 2014, ISBN 978-3-319-08262-2.
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Feedback

• Andrew Kramer, Prescient Healthcare Consulting, USA, ``I've been using your MOR book and found it very helpful in the area of critical care research'', September, 2020.
• Matthieu Vignes, Massey University, New Zealand, book review: ``a must-have for graduate students and any researchers willing to delve into new algorithms to solve their optimisation tasks. I was pleasantly impressed by the book's organisation and clarity, by its comprehensive and coherent topic coverage, and by its many outstanding numerical illustrations using R.'', June, 2017.
• Amazon reviews:

  ``I would like to congratulate Professor Cortez for a wonderful optimization book that uses R. ... I highly recommend this book for students, researchers, and hobbyists who are interested in implemented optimization code in R.'', December 2014

  ``it is an excellent companion book for operations research or optimization method textbooks'', February 23, 2017.

• Diego Ruiz, The University of Manchester, UK, book review: ``This book makes a good contribution in to the literature of modern optimization. It is well written and structured.'', March, 2016.
• Oliver Flasch (co-author of the rgp package): ``Congratulations on the result, it was a great pleasure reading it!''.
• Indir Jaganjac (via Linked-in):''R solutions code and data on your home page is excellent, especially simulated annealing and particle swarm optimization.''.
• KDnuggets post on 2014/10.

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