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Sequential Approximate Multiobjective Optimization Using Computational Intelligence [electronic resource] / by Min Yoon, Yeboon Yun, Hirotaka Nakayama.

By: Yoon, Min [author.].
Contributor(s): Yun, Yeboon [author.] | Nakayama, Hirotaka [author.] | SpringerLink (Online service).
Material type: TextTextSeries: Vector Optimization: Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2009Description: XVI, 200 p. 111 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540889106.Subject(s): Information theory | Operations research | Mathematical optimization | Computational complexity | Mathematical Modeling and Industrial Mathematics | Operations Research, Management Science | Theory of Computation | Operations Research/Decision Theory | Optimization | Discrete Mathematics in Computer ScienceAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 003.3 Online resources: Click here to access online
Contents:
Basic Concepts of Multi-objective Optimization -- Interactive Programming Methods for Multi-objective Optimization -- Generation of Pareto Frontier by Genetic Algorithms -- Multi-objective Optimization and Computational Intelligence -- Sequential Approximate Optimization -- Combining Aspiration Level Approach and SAMO -- Engineering Applications.
In: Springer eBooksSummary: This book highlights a new direction of multiobjective optimzation, which has never been treated in previous publications. When the function form of objective functions is not known explicitly as encountered in many practical problems, sequential approximate optimization based on metamodels is an effective tool from a practical viewpoint. Several sophisticated methods for sequential approximate multiobjective optimization using computational intelligence are introduced along with real applications, mainly engineering problems, in this book.
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Basic Concepts of Multi-objective Optimization -- Interactive Programming Methods for Multi-objective Optimization -- Generation of Pareto Frontier by Genetic Algorithms -- Multi-objective Optimization and Computational Intelligence -- Sequential Approximate Optimization -- Combining Aspiration Level Approach and SAMO -- Engineering Applications.

This book highlights a new direction of multiobjective optimzation, which has never been treated in previous publications. When the function form of objective functions is not known explicitly as encountered in many practical problems, sequential approximate optimization based on metamodels is an effective tool from a practical viewpoint. Several sophisticated methods for sequential approximate multiobjective optimization using computational intelligence are introduced along with real applications, mainly engineering problems, in this book.

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