標(biāo)題: Titlebook: Genetic Algorithms and Fuzzy Multiobjective Optimization; Masatoshi Sakawa Book 2002 Kluwer Academic Publishers 2002 addition.algorithms.l [打印本頁] 作者: 對(duì)將來事件 時(shí)間: 2025-3-21 16:13
書目名稱Genetic Algorithms and Fuzzy Multiobjective Optimization影響因子(影響力)
書目名稱Genetic Algorithms and Fuzzy Multiobjective Optimization影響因子(影響力)學(xué)科排名
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書目名稱Genetic Algorithms and Fuzzy Multiobjective Optimization網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Genetic Algorithms and Fuzzy Multiobjective Optimization被引頻次
書目名稱Genetic Algorithms and Fuzzy Multiobjective Optimization被引頻次學(xué)科排名
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書目名稱Genetic Algorithms and Fuzzy Multiobjective Optimization讀者反饋
書目名稱Genetic Algorithms and Fuzzy Multiobjective Optimization讀者反饋學(xué)科排名
作者: Encoding 時(shí)間: 2025-3-21 20:58
Genetic Algorithms and Fuzzy Multiobjective Optimization作者: 放逐某人 時(shí)間: 2025-3-22 02:53
Operations Research/Computer Science Interfaces Series382437.jpg作者: 伴隨而來 時(shí)間: 2025-3-22 07:21
Book 2002mming, and job-shop scheduling problems under multiobjectivenessand fuzziness. In addition, the book treats a wide range of actualreal world applications. The theoretical material and applicationsplace special stress on interactive decision-making aspects of fuzzymultiobjective optimization for huma作者: Inferior 時(shí)間: 2025-3-22 10:35 作者: 罐里有戒指 時(shí)間: 2025-3-22 13:41
Hans-Christian Riekhof (Professur)1970s as stochastic search techniques based on the mechanism of natural selection and natural genetics. In his 1975 monograph . [75], Holland presented genetic algorithms as an abstraction of biological evolution with a theoretical framework for adaptation. In the same year, De Jong completed his di作者: 罐里有戒指 時(shí)間: 2025-3-22 18:11 作者: corpus-callosum 時(shí)間: 2025-3-22 22:25
https://doi.org/10.1007/978-3-8349-9746-3h the introduction of a double string representation and the corresponding decoding algorithm, it is shown that a potential solution satisfying constraints can be obtained for each individual. Then the GADS are extended to deal with more general 0–1 programming problems involving both positive and n作者: 寬大 時(shí)間: 2025-3-23 03:29
Knowledge Transfer and Sharing, problems are formulated by assuming that the decision maker may have a fuzzy goal for each of the objective functions. Through the combination of the desirable features of both the interactive fuzzy satisficing methods for continuous variables and the genetic algorithms with double strings (GADS) d作者: Wernickes-area 時(shí)間: 2025-3-23 06:25
The benefits of winning customer loyalty,with integer 0–1 programming problems. New decoding algorithms for double strings using reference solutions with the reference solution updating procedure are proposed especially so that individuals are decoded to the corresponding feasible solution for integer programming problems. The chapter also作者: palette 時(shí)間: 2025-3-23 10:58 作者: JIBE 時(shí)間: 2025-3-23 17:42 作者: jumble 時(shí)間: 2025-3-23 20:25 作者: intangibility 時(shí)間: 2025-3-23 22:38 作者: LURE 時(shí)間: 2025-3-24 03:29 作者: Adj異類的 時(shí)間: 2025-3-24 06:50
Customer Relationship Managements of genetic algorithms to flexible scheduling for a machining center, in the operation planning of district heating and cooling (DHC) plants, and in the coal purchase planning in a real electric power plant.作者: 雄偉 時(shí)間: 2025-3-24 11:39
978-1-4613-5594-6Kluwer Academic Publishers 2002作者: 豐滿有漂亮 時(shí)間: 2025-3-24 17:53
Genetic Algorithms and Fuzzy Multiobjective Optimization978-1-4615-1519-7Series ISSN 1387-666X Series E-ISSN 2698-5489 作者: slipped-disk 時(shí)間: 2025-3-24 20:37
The benefits of winning customer loyalty,with integer 0–1 programming problems. New decoding algorithms for double strings using reference solutions with the reference solution updating procedure are proposed especially so that individuals are decoded to the corresponding feasible solution for integer programming problems. The chapter also includes several numerical experiments.作者: 專心 時(shí)間: 2025-3-25 02:29
Creating and Sustaining Loyalty Advantage,ter 3. Through the use of genetic algorithms with double strings (GADS), considerable effort is devoted to the development of fuzzy multiobjective integer programming as well as fuzzy multiobjective integer programming with fuzzy numbers together with several numerical experiments.作者: POINT 時(shí)間: 2025-3-25 06:33 作者: Receive 時(shí)間: 2025-3-25 08:43
Customer Relationship Managements of genetic algorithms to flexible scheduling for a machining center, in the operation planning of district heating and cooling (DHC) plants, and in the coal purchase planning in a real electric power plant.作者: 使迷惑 時(shí)間: 2025-3-25 11:51 作者: garrulous 時(shí)間: 2025-3-25 18:18
Fuzzy Multiobjective Integer Programming,ter 3. Through the use of genetic algorithms with double strings (GADS), considerable effort is devoted to the development of fuzzy multiobjective integer programming as well as fuzzy multiobjective integer programming with fuzzy numbers together with several numerical experiments.作者: 致詞 時(shí)間: 2025-3-25 22:35 作者: Mutter 時(shí)間: 2025-3-26 01:48
Some Applications,s of genetic algorithms to flexible scheduling for a machining center, in the operation planning of district heating and cooling (DHC) plants, and in the coal purchase planning in a real electric power plant.作者: 好忠告人 時(shí)間: 2025-3-26 05:12
Introduction,1970s as stochastic search techniques based on the mechanism of natural selection and natural genetics. In his 1975 monograph . [75], Holland presented genetic algorithms as an abstraction of biological evolution with a theoretical framework for adaptation. In the same year, De Jong completed his di作者: nonplus 時(shí)間: 2025-3-26 08:32
Foundations of Genetic Algorithms,ons and definitions in genetic algorithms, fundamental procedures of genetic algorithms are outlined. The main idea of genetic algorithms, involving coding, fitness, scaling, and genetic operators, is then examined. In the context of bit string representations, some of the important genetic operator作者: 貞潔 時(shí)間: 2025-3-26 16:35 作者: FIN 時(shí)間: 2025-3-26 20:13 作者: byline 時(shí)間: 2025-3-26 21:27
Genetic Algorithms for Integer Programming,with integer 0–1 programming problems. New decoding algorithms for double strings using reference solutions with the reference solution updating procedure are proposed especially so that individuals are decoded to the corresponding feasible solution for integer programming problems. The chapter also作者: 剝皮 時(shí)間: 2025-3-27 03:19 作者: Modify 時(shí)間: 2025-3-27 05:50
Genetic Algorithms for Nonlinear Programming, COnstrained Problems (GENOCOP) system for linear constraints, the coevolutionary genetic algorithm, called GENOCOP III, proposed by Michalewicz et al. is discussed in detail. Realizing some drawbacks of GENOCOP III, the coevolutionary genetic algorithm, called the revised GENOCOP III, is presented 作者: 毀壞 時(shí)間: 2025-3-27 11:07 作者: Contend 時(shí)間: 2025-3-27 16:29 作者: Rotator-Cuff 時(shí)間: 2025-3-27 18:23 作者: 身體萌芽 時(shí)間: 2025-3-27 23:45 作者: 偽證 時(shí)間: 2025-3-28 04:23 作者: 退出可食用 時(shí)間: 2025-3-28 08:11 作者: 的是兄弟 時(shí)間: 2025-3-28 14:12
https://doi.org/10.1007/978-3-8349-9746-3ference solution updating procedure are introduced so that each individual is decoded to the corresponding feasible solution for the general 0–1 programming problems. The detailed comparative numerical experiments with a branch and bound method are also provided.作者: OTTER 時(shí)間: 2025-3-28 16:25 作者: 聯(lián)想記憶 時(shí)間: 2025-3-28 20:33
Kundenbindung im neuen Jahrtausend,f the decision maker (DM), the fuzzy decision of Bellman and Zadeh is adopted for combining them. The genetic algorithm introduced in the previous chapter is extended for solving the formulated problems.作者: Crepitus 時(shí)間: 2025-3-29 00:05
,Genetic Algorithms for 0–1 Programming,ference solution updating procedure are introduced so that each individual is decoded to the corresponding feasible solution for the general 0–1 programming problems. The detailed comparative numerical experiments with a branch and bound method are also provided.作者: 漸變 時(shí)間: 2025-3-29 03:22
Genetic Algorithms for Nonlinear Programming, bisection method for generating a new feasible point on the line segment between a search point and a reference point efficiently. Illustrative numerical examples are provided to demonstrate the feasibility and efficiency of the revised GENOCOP III.作者: Melanocytes 時(shí)間: 2025-3-29 09:50
Fuzzy Multiobjective Job-Shop Scheduling,f the decision maker (DM), the fuzzy decision of Bellman and Zadeh is adopted for combining them. The genetic algorithm introduced in the previous chapter is extended for solving the formulated problems.作者: 提升 時(shí)間: 2025-3-29 13:30 作者: neuron 時(shí)間: 2025-3-29 18:05 作者: Ossification 時(shí)間: 2025-3-29 21:01
