Modern Optimization with R

Nowadays and with the increasing number of real world applications, many tasks can be viewed as an optimization problem.Sometimes, we are required to maximize or to minimize an objective function.The field of optimization can be used in different application domains.Optimization is part of the field of operations research which was developed with many classical techniques such as linear programming.In the last years, there have been new optimization algorithms, all of which are part of the term modern optimization.The book Modern Optimization with R describes these algorithms.Each chapter starts with an introduction, followed by the main content and ends with an R command summary and exercises.This book includes modern optimization methods combined with real applications.Examples in the area can be found of time series forecasting, data mining classification, and regression models.The book is divided into seven different chapters.A detailed description can be found below:Chapter 1 introduces the motivation for modern optimization methods and why R should be used to explore such methods.In this chapter, several key modern optimization topics can be found, namely the representation of a solution, the evaluation function, constraints, and an overall view of modern optimization methods.This chapter ends with the description of the optimization tasks that are used for tutorial purposes in the next chapter.Chapter 2 presents the basic concepts of R. For those people who are familiar with R, this chapter can be skipped.Chapter 3 details particularly three blind search approaches: pure blind, grid, and Monte Carlo search.Chapter 4 introduces local search methods, namely hill climbing, simulated annealing, and tabu search.An example comparison between several local search methods is shown, the implementation in R is also considered.Chapter 5 presents population-based search methods, namely genetic and evolutionary algorithms, differential evolution, particle swarm optimization, and estimation of distribution

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