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).
Author(s): Paulo Cortez
Series: Use R!
Edition: 2
Publisher: Springer Nature Switzerland AG
Year: 2021
Language: English
Pages: 254
City: Cham
Tags: etaheuristics, Optimization
Preface
First Edition Feedback
Updated and Revised Second Edition
How to Read This Book
Production
Contents
List of Figures
List of Algorithms
1 Introduction
1.1 Motivation
1.2 Why R?
1.3 Representation of a Solution
1.4 Evaluation Function
1.5 Constraints
1.6 Optimization Methods
1.7 Limitations and Criticism
1.8 Demonstrative Problems
2 R Basics
2.1 Introduction
2.2 Basic Objects and Functions
2.3 Controlling Execution and Writing Functions
2.4 Importing and Exporting Data
2.5 Additional Features
Command Line Execution
Parallel Computing
Source Code of a Function
Interfacing with Other Languages
Interactive Web Applications
2.6 Command Summary
2.7 Exercises
3 Blind Search
3.1 Introduction
3.2 Full Blind Search
3.3 Grid Search
3.4 Monte Carlo Search
3.5 Command Summary
3.6 Exercises
4 Local Search
4.1 Introduction
4.2 Hill Climbing
4.3 Simulated Annealing
4.4 Tabu Search
4.5 Comparison of Local Search Methods
4.6 Tuning Optimization Parameters
4.7 Command Summary
4.8 Exercises
5 Population Based Search
5.1 Introduction
5.2 Genetic and Evolutionary Algorithms
5.3 Differential Evolution
5.4 Particle Swarm Optimization
5.5 Ant Colony Optimization
5.6 Estimation of Distribution Algorithm
5.7 Comparison of Population Based Methods
5.8 Bag Prices with Constraint
5.9 Parallel Execution of Population Based Methods
5.10 Genetic Programming
5.11 Grammatical Evolution
5.12 Command Summary
5.13 Exercises
6 Multi-Objective Optimization
6.1 Introduction
6.2 Multi-Objective Demonstrative Problems
6.3 Weighted-Formula Approach
6.4 Lexicographic Approach
6.5 Pareto Approach
6.6 Command Summary
6.7 Exercises
7 Applications
7.1 Introduction
7.2 Traveling Salesman Problem
7.3 Time Series Forecasting
7.4 Wine Quality Classification
7.5 Command Summary
7.6 Exercises
References
Solutions
Exercises of Chapter 2
Exercises of Chapter 3
Exercises of Chapter 4
Exercises of Chapter 5
Exercises of Chapter 6
Exercises of Chapter 7
Index
