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Titlebook: Evolutionary Algorithms for Solving Multi-Objective Problems; Carlos A. Coello Coello,David A. Veldhuizen,Gary B Book 20021st edition Spri

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發(fā)表于 2025-3-21 17:02:30 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Evolutionary Algorithms for Solving Multi-Objective Problems
編輯Carlos A. Coello Coello,David A. Veldhuizen,Gary B
視頻videohttp://file.papertrans.cn/318/317813/317813.mp4
叢書名稱Genetic Algorithms and Evolutionary Computation
圖書封面Titlebook: Evolutionary Algorithms for Solving Multi-Objective Problems;  Carlos A. Coello Coello,David A. Veldhuizen,Gary B Book 20021st edition Spri
描述Researchers and practitioners alike are increasingly turning to search, op- timization, and machine-learning procedures based on natural selection and natural genetics to solve problems across the spectrum of human endeavor. These genetic algorithms and techniques of evolutionary computation are solv- ing problems and inventing new hardware and software that rival human designs. The Kluwer Series on Genetic Algorithms and Evolutionary Computation pub- lishes research monographs, edited collections, and graduate-level texts in this rapidly growing field. Primary areas of coverage include the theory, implemen- tation, and application of genetic algorithms (GAs), evolution strategies (ESs), evolutionary programming (EP), learning classifier systems (LCSs) and other variants of genetic and evolutionary computation (GEC). The series also pub- lishes texts in related fields such as artificial life, adaptive behavior, artificial immune systems, agent-based systems, neural computing, fuzzy systems, and quantum computing as long as GEC techniques are part of or inspiration for the system being described. This encyclopedic volume on the use of the algorithms of genetic and evolu- tionary com
出版日期Book 20021st edition
關(guān)鍵詞algorithms; chemistry; classification; computation; computer; computer science; ecology; evolution; evolutio
版次1
doihttps://doi.org/10.1007/978-1-4757-5184-0
isbn_ebook978-1-4757-5184-0Series ISSN 1568-2587
issn_series 1568-2587
copyrightSpringer Science+Business Media New York 2002
The information of publication is updating

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Erythrocytes as Drug Carriers in Medicinebut also to employ the honest selection of appropriate testing metrics and associated statistical evaluation and comparison. In this chapter, the development of various MOP test suites is addressed, and in the next chapter, their use in appropriate MOEA evaluations is discussed.
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Action of Drugs on the Erythrocyte Membrane,heuristic methods, providing guidelines for reporting results and ensuring their reproducibility. Specifically, they suggest that a well-designed experiment follow these steps: .. This chapter applies these concepts in developing experimental MOEA testing procedures using appropriate MOP test suites from Chapter 3.
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https://doi.org/10.1007/978-3-663-07212-65.1 lists contemporary efforts reflecting MOEA theory development. In essence, an MOEA is searching for optimal elements in a partially ordered set or in the Pareto optimal set. Thus, the concept of convergence to .. and .. is integral to the MOEA search process.
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Evolutionary Algorithm MOP Approaches,rk. For researchers, this is the normal procedure to trigger original contributions. For practitioners, this knowledge of the area allows them to choose the most appropriate algorithm(s) for their specific application.
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MOEA Test Suites,nction, an MOP test suite, pedagogical functions, or a real-world problem? How to find an appropriate MOEA test? MOEA literature, historical use, test generators, or well known real-world applications. When to test? Incremental algorithm and test development starting early or wait until end of devel
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