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2014 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Modern Optimization with R

verfasst von: Paulo Cortez

Verlag: Springer International Publishing

Buchreihe : Use R!

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Wirtschaft" , Springer Professional "Technik"

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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
A vast number of real-world (often complex) tasks can be viewed as an optimization problem, where the goal is to minimize or maximize a given goal. In effect, optimization is quite useful in distinct application domains, such as Agriculture, Banking, Control, Engineering, Finance, Marketing, Production and Science. Moreover, due to advances in Information Technology, nowadays it is easy to store and process data. Since the 1970s, and following the Moore’s law, the number of transistors in computer processors has doubled every 2 years, resulting in more computational power at a reasonable price. And it is estimated that the amount of data storage doubles at a higher rate. Furthermore, organizations and individual users are currently pressured to increase efficiency and reduce costs. Rather than taking decisions based on human experience and intuition, there is an increasing trend for adopting computational tools, based on optimization methods, to analyze real-world data in order to make better informed decisions.
Paulo Cortez
Chapter 2. R Basics
Abstract
As explained in the preface of this book, the goal of this chapter is to briefly present the most relevant R tool aspects that need to be learned by non-experts in order to understand the examples discussed in this book. For a more detailed introduction to the tool, please consult (Paradis 2002; Zuur et al. 2009; Venables et al. 2013).
Paulo Cortez
Chapter 3. Blind Search
Abstract
Full blind search assumes the exhaustion of all alternatives, where any previous search does not affect how next solutions are tested (left of Fig. 3.1). Given that the full search space is tested, the optimum solution is always found. Blind search is only applicable to discrete search spaces and it is easy to encode in two ways. First, by setting the full search space in a matrix and then sequentially testing each row (solution) of this matrix. Second, in a recursive way, by setting the search space as a tree, where each branch denotes a possible value for a given variable and all solutions appear at the leaves (at the same level). Examples of two quite known blind methods based on tree structures are depth-first and breadth-first algorithms. The former starts at the root of the tree and traverses through each branch as far as possible, before backtracking. The latter also starts at the root but searches on a level basis, searching first all succeeding nodes of the root and then the next succeeding nodes of the root succeeding nodes, and so on.
Paulo Cortez
Chapter 4. Local Search
Abstract
In contrast with the blind search methods presented in Chap. 3, modern optimization techniques are based on a guided search, where new solutions are generated from existing solutions. Local search, often termed single-state search, includes several methods that focus their attention within a local neighborhood of a given initial solution, as shown in Fig. 4.1.
Paulo Cortez
Chapter 5. Population Based Search
Abstract
In previous chapter, several local based search methods were presented, such as hill climbing, simulated annealing, and tabu search. All these methods are single-state, thus operating their effort around the neighborhood of a current solution. This type of search is simple and quite often efficient (Michalewicz et al. 2006).
Paulo Cortez
Chapter 6. Multi-Objective Optimization
Abstract
In previous chapters, only single objective tasks were addressed. However, multiple goals are common in real-world domains. For instance, a company typically desires to increase sales while reducing production costs. Within its marketing department, the goal might include maximizing target audiences while minimizing the marketing budget. Also, within the production department, the same company might want to maximize the manufactured items, in terms of both quality and production numbers, while minimizing production time, costs, and waste of material. Often, the various objectives can conflict, where gaining in one goal involves losing in another one.
Paulo Cortez
Chapter 7. Applications
Abstract
Previous chapters have approached demonstrative optimization tasks that were synthetically generated. The intention was to present a tutorial perspective and thus more simpler tasks were approached. As a complement, this chapter addresses real-world applications, where the data available is taken from a physical phenomena. Exemplifying the optimization of real-world data in R is interesting for two main reasons. First, physical phenomena may contain surprising or unknown features.
Paulo Cortez
Backmatter
Metadaten
Titel
Modern Optimization with R
verfasst von
Paulo Cortez
Copyright-Jahr
2014
Electronic ISBN
978-3-319-08263-9
Print ISBN
978-3-319-08262-2
DOI
https://doi.org/10.1007/978-3-319-08263-9

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