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190420s2019 ts a ob 001 0 eng d |
020 |
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|a9781681087054|q(electronic bk.)
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020 |
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|z9781681087061
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040 |
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|aEBLCP|beng|cEBLCP|dOCLCQ|dYDX|dN|T
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041 |
0
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|aeng
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050 |
4
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|aQA9.58
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082 |
04
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|a511.8|223
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100 |
1
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|aKeller, André A.
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245 |
10
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|aMulti-objective optimization in theory and practice II|h[electronic resource] :|bmetaheuristic algorithms /|cauthored by André A. Keller.
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250 |
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|a1st ed.
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260 |
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|aSharjah, UAE :|bBentham Science Publishers,|c2019.
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300 |
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|a1 online resource (310 p.) :|bill.
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504 |
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|aIncludes bibliographical references at the end of each chapters and index.
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520 |
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|aMulti-Objective Optimization in Theory and Practice is a simplified two-part approach to optimization (MOO) problems. This second part focuses on the use of metaheuristic algorithms in more challenging practical cases. The book includes ten chapters that cover several advanced MOO techniques. These include the determination of Pareto-optimal sets of solutions, metaheuristic algorithms, genetic search algorithms and evolution strategies, decomposition algorithms, hybridization of different metaheuristics, and many-objective (more than three objectives) optimization and parallel computation. The final section of the book presents information about the design and types of fifty test problems for which the Pareto-optimal front is approximated. For each of them, the package NSGA-II is used to approximate the Pareto-optimal front. It is an essential handbook for students and teachers involved in advanced optimization courses in engineering, information science and mathematics degree programs.
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588 |
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|aDescription based on print version record.
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650 |
0
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|aAlgorithms.
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650 |
0
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|aMetaheuristics.
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856 |
40
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|uhttps://doi.org/10.2174/97816810870541190101
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