000 02299nam a22003375i 4500
001 91987
005 20231128103824.0
010 _a978-3-030-72819-9
_dcompra
090 _a91987
100 _a20231023d2021 k||y0pory50 ba
101 _aeng
102 _aCH
200 _aModern optimization with R
_bDocumento eletrĂ³nico
_fby Paulo Cortez
205 _a2nd ed.
210 _aCham
_cSpringer
_d2021
215 _aXVII, 254 p.
_cil.
225 _aUse R!
303 _aThe 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. 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). .
606 _aMathematical statistics
_xData processing
606 _aMathematical optimization
606 _aArtificial intelligence
_xData processing
606 _aSampling (Statistics)
606 _aArtificial intelligence
606 _aProgramming languages (Electronic computers)
680 _aQA276.4-.45
700 _aCortez
_bPaulo
_947640
801 _aPT
_gRPC
856 _uhttps://doi.org/10.1007/978-3-030-72819-9
942 _2lcc
_cF
_n0