slim bechikh (32 résultats)

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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-and coming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include: optimization in dynamic environments, multi-objective bilevel programming, handling high dimensionality under many objectives, and evolutionary multitasking. In addition to theory and methodology, this book describes several real-world applications from various domains, which will expose the readers to the versatility of evolutionary multi-objective optimization.

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-and coming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include: optimization in dynamic environments, multi-objective bilevel programming, handling high dimensionality under many objectives, and evolutionary multitasking. In addition to theory and methodology, this book describes several real-world applications from various domains, which will expose the readers to the versatility of evolutionary multi-objective optimization.

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Taschenbuch. Etat : Neu. Neuware -This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-and coming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include: optimization in dynamic environments, multi-objective bilevel programming, handling high dimensionality under many objectives, and evolutionary multitasking. In addition to theory and methodology, this book describes several real-world applications from various domains, which will expose the readers to the versatility of evolutionary multi-objective optimization.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 192 pp. Englisch.

Buch. Etat : Neu. Neuware -This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-and coming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include: optimization in dynamic environments, multi-objective bilevel programming, handling high dimensionality under many objectives, and evolutionary multitasking. In addition to theory and methodology, this book describes several real-world applications from various domains, which will expose the readers to the versatility of evolutionary multi-objective optimization.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 192 pp. Englisch.

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Hardcover. Etat : Brand New. 192 pages. 9.25x6.25x0.50 inches. In Stock.

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Bechikh SlimSlim Bechikh received the B.S. degree in computer science applied to management and the M.S. degree in modeling from the High Institute of Management of Tunis, University of Tunis, Tunis, Tunisia, in2006 and 2008, respect.

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Les Algorithmes Evolutionnaires (AEs) représentent une famille d'algorithmes fondés sur la théorie Darwinienne. Ils font évoluer une population d'individus vers l'optimum d'une fonction. Ils sont caractérisés par leur capacité de diriger la recherche vers les zones prometteuses. Cependant, ces métaheuristiques possèdent quelques faiblesses. Une façon de les surmonter est de combiner ces AEs avec d'autres méthodes de recherche, ce phénomène est appelé l'hybridation. La plus connue est l'hybridation avec les méthodes de recherche locale (RL) qui ont la capacité à détecter les optima locaux. Le résultat d'une telle hybridation est appelé Algorithme Mémétique (AM). Plusieurs AMs ont montré de très bons résultats dans la résolution de problèmes réels dans un cadre mono-objectif. C'est pour cette raison, la communauté scientifique a opté vers le développement d'AMs pour le cadre multi-objectif. La plupart des AMs multi-objectifs visent la résolution des problèmes réels plutôt que le développement du cadre conceptuel de tels algorithmes. Dans ce travail, nous présentons les problèmes de conception des AMs multi-objectifs. Ainsi, nous développons un AM multi-objectif (PHC-NSGA-II).

PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Les Algorithmes Evolutionnaires (AEs) représentent une famille d'algorithmes fondés sur la théorie Darwinienne. Ils font évoluer une population d'individus vers l'optimum d'une fonction. Ils sont caractérisés par leur capacité de diriger la recherche vers les zones prometteuses. Cependant, ces métaheuristiques possèdent quelques faiblesses. Une façon de les surmonter est de combiner ces AEs avec d'autres méthodes de recherche, ce phénomène est appelé l'hybridation. La plus connue est l'hybridation avec les méthodes de recherche locale (RL) qui ont la capacité à détecter les optima locaux. Le résultat d'une telle hybridation est appelé Algorithme Mémétique (AM). Plusieurs AMs ont montré de très bons résultats dans la résolution de problèmes réels dans un cadre mono-objectif. C'est pour cette raison, la communauté scientifique a opté vers le développement d'AMs pour le cadre multi-objectif. La plupart des AMs multi-objectifs visent la résolution des problèmes réels plutôt que le développement du cadre conceptuel de tels algorithmes. Dans ce travail, nous présentons les problèmes de conception des AMs multi-objectifs. Ainsi, nous développons un AM multi-objectif (PHC-NSGA-II) 104 pp. Französisch.

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Les Algorithmes Evolutionnaires (AEs) représentent une famille d¿algorithmes fondés sur la théorie Darwinienne. Ils font évoluer une population d¿individus vers l¿optimum d'une fonction. Ils sont caractérisés par leur capacité de diriger la recherche vers les zones prometteuses. Cependant, ces métaheuristiques possèdent quelques faiblesses. Une façon de les surmonter est de combiner ces AEs avec d¿autres méthodes de recherche, ce phénomène est appelé l¿hybridation. La plus connue est l¿hybridation avec les méthodes de recherche locale (RL) qui ont la capacité à détecter les optima locaux. Le résultat d¿une telle hybridation est appelé Algorithme Mémétique (AM). Plusieurs AMs ont montré de très bons résultats dans la résolution de problèmes réels dans un cadre mono-objectif. C¿est pour cette raison, la communauté scientifique a opté vers le développement d¿AMs pour le cadre multi-objectif. La plupart des AMs multi-objectifs visent la résolution des problèmes réels plutôt que le développement du cadre conceptuel de tels algorithmes. Dans ce travail, nous présentons les problèmes de conception des AMs multi-objectifs. Ainsi, nous développons un AM multi-objectif (PHC-NSGA-II)VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 104 pp. Französisch.

Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides both methodological treatments and real world insightsServes as comprehensive reference for researchers, practitioners, and advanced-level students Covers both the theory and practice of using evolutionary algorithms in tackling rea.

Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides both methodological treatments and real world insightsServes as comprehensive reference for researchers, practitioners, and advanced-level students Covers both the theory and practice of using evolutionary algorithms in tackling rea.

Etat : New. Print on Demand pp. 104 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Etat : New. PRINT ON DEMAND pp. 104.

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-and coming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include: optimization in dynamic environments, multi-objective bilevel programming, handling high dimensionality under many objectives, and evolutionary multitasking. In addition to theory and methodology, this book describes several real-world applications from various domains, which will expose the readers to the versatility of evolutionary multi-objective optimization. 192 pp. Englisch.

Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-and coming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include: optimization in dynamic environments, multi-objective bilevel programming, handling high dimensionality under many objectives, and evolutionary multitasking. In addition to theory and methodology, this book describes several real-world applications from various domains, which will expose the readers to the versatility of evolutionary multi-objective optimization. 192 pp. Englisch.