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Biologically inspired optimization methods: an introduction

The advent of rapid, reliable and cheap computing power over the last decades has transformed many, if not most, fields of science and engineering. The multidisciplinary field of optimization is no exception. First of all, with fast computers, researchers and engineers can apply classical optimizati...

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Detalles Bibliográficos
Autor principal: Wahde, M
Lenguaje:eng
Publicado: WIT Press 2008
Materias:
Acceso en línea:http://cds.cern.ch/record/2303801
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author Wahde, M
author_facet Wahde, M
author_sort Wahde, M
collection CERN
description The advent of rapid, reliable and cheap computing power over the last decades has transformed many, if not most, fields of science and engineering. The multidisciplinary field of optimization is no exception. First of all, with fast computers, researchers and engineers can apply classical optimization methods to problems of larger and larger size. In addition, however, researchers have developed a host of new optimization algorithms that operate in a rather different way than the classical ones, and that allow practitioners to attack optimization problems where the classical methods are either not applicable or simply too costly (in terms of time and other resources) to apply.This book is intended as a course book for introductory courses in stochastic optimization algorithms (in this book, the terms optimization method and optimization algorithm will be used interchangeably), and it has grown from a set of lectures notes used in courses, taught by the author, at the international master programme Complex Adaptive Systems at Chalmers University of Technology in Göteborg, Sweden. Thus, a suitable audience for this book are third and fourth-year engineering students, with a background in engineering mathematics (analysis, algebra, and probability theory) as well as some knowledge of computer programming.
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spelling cern-23038012021-04-21T18:54:41Zhttp://cds.cern.ch/record/2303801engWahde, MBiologically inspired optimization methods: an introductionMathematical Physics and MathematicsThe advent of rapid, reliable and cheap computing power over the last decades has transformed many, if not most, fields of science and engineering. The multidisciplinary field of optimization is no exception. First of all, with fast computers, researchers and engineers can apply classical optimization methods to problems of larger and larger size. In addition, however, researchers have developed a host of new optimization algorithms that operate in a rather different way than the classical ones, and that allow practitioners to attack optimization problems where the classical methods are either not applicable or simply too costly (in terms of time and other resources) to apply.This book is intended as a course book for introductory courses in stochastic optimization algorithms (in this book, the terms optimization method and optimization algorithm will be used interchangeably), and it has grown from a set of lectures notes used in courses, taught by the author, at the international master programme Complex Adaptive Systems at Chalmers University of Technology in Göteborg, Sweden. Thus, a suitable audience for this book are third and fourth-year engineering students, with a background in engineering mathematics (analysis, algebra, and probability theory) as well as some knowledge of computer programming.WIT Pressoai:cds.cern.ch:23038012008
spellingShingle Mathematical Physics and Mathematics
Wahde, M
Biologically inspired optimization methods: an introduction
title Biologically inspired optimization methods: an introduction
title_full Biologically inspired optimization methods: an introduction
title_fullStr Biologically inspired optimization methods: an introduction
title_full_unstemmed Biologically inspired optimization methods: an introduction
title_short Biologically inspired optimization methods: an introduction
title_sort biologically inspired optimization methods: an introduction
topic Mathematical Physics and Mathematics
url http://cds.cern.ch/record/2303801
work_keys_str_mv AT wahdem biologicallyinspiredoptimizationmethodsanintroduction