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Titlebook: Evolutionary Multiobjective Optimization; Theoretical Advances Ajith Abraham,Lakhmi Jain,Robert Goldberg Book 2005 Springer-Verlag London 2

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发表于 2025-3-21 16:31:44 | 显示全部楼层 |阅读模式
书目名称Evolutionary Multiobjective Optimization
副标题Theoretical Advances
编辑Ajith Abraham,Lakhmi Jain,Robert Goldberg
视频videohttp://file.papertrans.cn/318/317991/317991.mp4
概述Offers the first-ever comprehensive treatment of the developmental as well as application aspects of the "cutting edge” field of evolutionary computation based multi-criteria optimisation.The only vol
丛书名称Advanced Information and Knowledge Processing
图书封面Titlebook: Evolutionary Multiobjective Optimization; Theoretical Advances Ajith Abraham,Lakhmi Jain,Robert Goldberg Book 2005 Springer-Verlag London 2
描述.Evolutionary Multiobjective Optimization. is a rare collection of the latest state-of-the-art theoretical research, design challenges and applications in the field of multiobjective optimization paradigms using evolutionary algorithms. It includes two introductory chapters giving all the fundamental definitions, several complex test functions and a practical problem involving the multiobjective optimization of space structures under static and seismic loading conditions used to illustrate the various multiobjective optimization concepts. ..Important features include:...Detailed overview of all the multiobjective optimization paradigms using evolutionary algorithms..Excellent coverage of timely, advanced multiobjective optimization topics..State-of-the-art theoretical research and application developments..Chapters authored by pioneers in the field ..Academics and industrial scientists as well as engineers engaged in research, development and application of evolutionary algorithm based Multiobjective Optimization will find the comprehensive coverage of this book invaluable..
出版日期Book 2005
关键词Computer; Data Structures; Genetic Algorithms; Multi-Criteria Optimization; algorithms; automata; evolutio
版次1
doihttps://doi.org/10.1007/1-84628-137-7
isbn_softcover978-1-84996-916-1
isbn_ebook978-1-84628-137-2Series ISSN 1610-3947 Series E-ISSN 2197-8441
issn_series 1610-3947
copyrightSpringer-Verlag London 2005
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Experimenting with Raspberry Piing. In this introductory chapter, some fundamental concepts of multiobjective optimization are introduced, emphasizing the motivation and advantages of using evolutionary algorithms. We then lay out the important contributions of the remaining chapters of this volume.
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https://doi.org/10.1007/978-1-4899-6635-3uctures are evaluated and compared on several multiobjective example problems. The results presented show that typically, linear lists perform better for small population sizes and higher-dimensional Pareto fronts (large archives) whereas Quad-trees perform better for larger population sizes and Pareto sets of small cardinality.
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Quad-trees: A Data Structure for Storing Pareto Sets in Multiobjective Evolutionary Algorithms withuctures are evaluated and compared on several multiobjective example problems. The results presented show that typically, linear lists perform better for small population sizes and higher-dimensional Pareto fronts (large archives) whereas Quad-trees perform better for larger population sizes and Pareto sets of small cardinality.
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The Transformative Power of Action Research,over to the multiobjective case, if a simple dominance-based selection scheme is used. As a solution, a combined strategy is proposed using dominance-based selection in the archive and scalarizing functions in the working population.
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Self-adaptation and Convergence of Multiobjective Evolutionary Algorithms in Continuous Search Spacover to the multiobjective case, if a simple dominance-based selection scheme is used. As a solution, a combined strategy is proposed using dominance-based selection in the archive and scalarizing functions in the working population.
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