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2021 | Book

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Modern Optimization with R

Author: Prof. Paulo Cortez

Publisher: Springer International Publishing

Book Series : Use R!

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

The goal of this book is to gather in a single work the most relevant concepts related in optimization methods, showing how such theories 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).

Table of Contents

Frontmatter
Chapter 1. Introduction
Abstract
This chapter first introduces the motivation for using modern optimization via the R tool. Then, three relevant aspects are discussed: how to represent a solution, how to evaluate the quality of solutions, and how to handle constraints. Next, an overall view of modern optimization methods is presented, followed by a discussion of their limitations and criticism. Finally, this chapter presents the optimization tasks that are used for tutorial purposes in the book.
Paulo Cortez
Chapter 2. R Basics
Abstract
This chapter presents the necessary knowledge for non-R-experts to understand and test the book code examples. First, more basic knowledge is introduced, namely objects and functions, how to control execution, and how to import and export data. Then, more advanced features are discussed, including command line execution, parallel computing, interfacing with other computer languages, and interactive web applications.
Paulo Cortez
Chapter 3. Blind Search
Abstract
This chapter discusses what is blind search and how it can be implemented using the R tool. In particular, it details two full blind search implementations, two grid search approaches (standard and nested) and a Monte Carlo (random) search.
Paulo Cortez
Chapter 4. Local Search
Abstract
This chapter introduces several local search methods and their R implementations, namely hill climbing (pure and steepest ascent and stochastic variants), simulated annealing, and tabu search. Then, the chapter demonstrates how local search methods can be compared. Finally, it shows how modern optimization methods (including local search ones) can be tuned in terms of their internal parameters by using an iterated racing.
Paulo Cortez
Chapter 5. Population Based Search
Abstract
This chapter introduces population based search methods and their R implementations, namely genetic and evolutionary algorithms, differential evolution, particle swarm optimization, ant colony optimization, estimation of distribution algorithm, genetic programming, and grammatical evolution. The chapter also presents examples of how to compare population based methods, how to handle constraints, and how to run population based methods in parallel.
Paulo Cortez
Chapter 6. Multi-Objective Optimization
Abstract
This chapter is dedicated to multi-objective optimization. First, three demonstrative multi-objective tasks are presented. Then, three main multi-objective approaches are discussed and demonstrated: weighted-formula, lexicographic, and Pareto (e.g., NSGA-II and NSGA-III, SMS-EMOA, AS-EMOA).
Paulo Cortez
Chapter 7. Applications
Abstract
This chapter presents real-world applications of previously discussed modern optimization methods: traveling salesman problem, time series forecasting, and wine quality classification.
Paulo Cortez
Backmatter
Metadata
Title
Modern Optimization with R
Author
Prof. Paulo Cortez
Copyright Year
2021
Electronic ISBN
978-3-030-72819-9
Print ISBN
978-3-030-72818-2
DOI
https://doi.org/10.1007/978-3-030-72819-9

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