標(biāo)題: Titlebook: Evolutionary Multi-objective Optimization in Uncertain Environments; Issues and Algorithm Chi-Keong Goh,Kay Chen Tan Book 2009 Springer-Ver [打印本頁(yè)] 作者: 乳缽 時(shí)間: 2025-3-21 17:15
書目名稱Evolutionary Multi-objective Optimization in Uncertain Environments影響因子(影響力)
書目名稱Evolutionary Multi-objective Optimization in Uncertain Environments影響因子(影響力)學(xué)科排名
書目名稱Evolutionary Multi-objective Optimization in Uncertain Environments網(wǎng)絡(luò)公開(kāi)度
書目名稱Evolutionary Multi-objective Optimization in Uncertain Environments網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書目名稱Evolutionary Multi-objective Optimization in Uncertain Environments被引頻次
書目名稱Evolutionary Multi-objective Optimization in Uncertain Environments被引頻次學(xué)科排名
書目名稱Evolutionary Multi-objective Optimization in Uncertain Environments年度引用
書目名稱Evolutionary Multi-objective Optimization in Uncertain Environments年度引用學(xué)科排名
書目名稱Evolutionary Multi-objective Optimization in Uncertain Environments讀者反饋
書目名稱Evolutionary Multi-objective Optimization in Uncertain Environments讀者反饋學(xué)科排名
作者: Vulnerary 時(shí)間: 2025-3-21 22:24
Evolutionary Multi-objective Optimization in Uncertain EnvironmentsIssues and Algorithm作者: IVORY 時(shí)間: 2025-3-22 03:55
Robust Evolutionary Multi-objective Optimizationt suite with features of noise-induced solution space, fitness landscape and decision space variation. In addition, the vehicle routing problem with stochastic demand (VRPSD) is presented a practical example of robust combinatorial multi-objective optimization problems.作者: Infinitesimal 時(shí)間: 2025-3-22 04:37 作者: Reclaim 時(shí)間: 2025-3-22 12:18 作者: Expressly 時(shí)間: 2025-3-22 16:14 作者: Expressly 時(shí)間: 2025-3-22 20:34
https://doi.org/10.1007/978-3-658-35330-8t suite with features of noise-induced solution space, fitness landscape and decision space variation. In addition, the vehicle routing problem with stochastic demand (VRPSD) is presented a practical example of robust combinatorial multi-objective optimization problems.作者: packet 時(shí)間: 2025-3-22 22:15 作者: uveitis 時(shí)間: 2025-3-23 03:20 作者: 嫌惡 時(shí)間: 2025-3-23 07:08 作者: phlegm 時(shí)間: 2025-3-23 11:05 作者: burnish 時(shí)間: 2025-3-23 16:48 作者: 松軟 時(shí)間: 2025-3-23 18:29 作者: 圍裙 時(shí)間: 2025-3-23 22:52
https://doi.org/10.1007/978-3-663-07215-7lutionary optimization has led to the development of evolutionary artificial neural networks (EANN) in which adaptation is performed primarily by means of evolution. Given that the intrinsic relationship between the architecture and the associated synaptic weights can be quite complex, the design me作者: 有害 時(shí)間: 2025-3-24 04:41
Grundlegende Auswertung der Messergebnisse,blems involving such dynamic systems, the fitness landscape changes to reflect the time-varying requirements of the systems. Examples of such problems can be found in the areas of control, scheduling, vehicle routing, and autonomous path planning.作者: Comedienne 時(shí)間: 2025-3-24 07:22 作者: Measured 時(shí)間: 2025-3-24 12:00 作者: colony 時(shí)間: 2025-3-24 16:15
Unternehmen als Institution und Organisationptimization considers the effects explicitly and seeks to minimize the consequences without eliminating efficiency. Many different approaches, including Taguchi orthogonal arrays, response surface methodology, probabilistic design analysis, have been applied for robust optimization. In operational r作者: mastoid-bone 時(shí)間: 2025-3-24 20:05
that when some data are random, it is no longer possible to require that all constraints be satisfied for all realizations of the random variables (170). In addition, the actual cost of a particular solution to the VRPSD cannot be known with certainty before the actual implementation of the solution作者: ICLE 時(shí)間: 2025-3-25 00:23
Experimenting with AVR Microcontrollersn with uncertainties. This goal has been accomplished through a relatively easy walk-through on the basic ideas found in the single-objective literature that can be applied to handle multi-objective noisy, dynamic and robust fitness landscapes. These ideas have been introduced in the context of unce作者: Adjourn 時(shí)間: 2025-3-25 04:39 作者: 竊喜 時(shí)間: 2025-3-25 10:46
Studies in Computational Intelligencehttp://image.papertrans.cn/e/image/317990.jpg作者: Magnificent 時(shí)間: 2025-3-25 12:59
978-3-642-10113-7Springer-Verlag Berlin Heidelberg 2009作者: hauteur 時(shí)間: 2025-3-25 16:37
Evolutionary Multi-objective Optimization in Uncertain Environments978-3-540-95976-2Series ISSN 1860-949X Series E-ISSN 1860-9503 作者: Banquet 時(shí)間: 2025-3-25 23:35 作者: 馬賽克 時(shí)間: 2025-3-26 00:45
https://doi.org/10.1007/978-3-658-40889-3In the previous chapter, we have shown empirically that the performance of MOEA deteriorates quickly with increasing noise intensities. As the results suggest, the canonical MOEA will face difficulties identifying non-dominated solutions, let alone maintaing a diverse set of near-optimal solutions.作者: 依法逮捕 時(shí)間: 2025-3-26 04:34
Handling Noise in Evolutionary Multi-objective OptimizationIn the previous chapter, we have shown empirically that the performance of MOEA deteriorates quickly with increasing noise intensities. As the results suggest, the canonical MOEA will face difficulties identifying non-dominated solutions, let alone maintaing a diverse set of near-optimal solutions.作者: 大溝 時(shí)間: 2025-3-26 09:39 作者: calumniate 時(shí)間: 2025-3-26 13:42 作者: 光明正大 時(shí)間: 2025-3-26 20:30
Handling Noise in Evolutionary Neural Network Designlutionary optimization has led to the development of evolutionary artificial neural networks (EANN) in which adaptation is performed primarily by means of evolution. Given that the intrinsic relationship between the architecture and the associated synaptic weights can be quite complex, the design me作者: Synchronism 時(shí)間: 2025-3-26 23:50
Dynamic Evolutionary Multi-objective Optimizationblems involving such dynamic systems, the fitness landscape changes to reflect the time-varying requirements of the systems. Examples of such problems can be found in the areas of control, scheduling, vehicle routing, and autonomous path planning.作者: 生銹 時(shí)間: 2025-3-27 02:35 作者: jagged 時(shí)間: 2025-3-27 05:57
Robust Evolutionary Multi-objective Optimizationth environmental changes. In such cases, it would be desirable to find solutions that perform reasonably well within some range of change. In fact, many real-world applications involve the simultaneous optimization of several competing objectives and are susceptible to decision or environmental para作者: 原來(lái) 時(shí)間: 2025-3-27 09:36
Evolving Robust Solutions in Multi-Objective Optimizationptimization considers the effects explicitly and seeks to minimize the consequences without eliminating efficiency. Many different approaches, including Taguchi orthogonal arrays, response surface methodology, probabilistic design analysis, have been applied for robust optimization. In operational r作者: 煩人 時(shí)間: 2025-3-27 15:52 作者: 加強(qiáng)防衛(wèi) 時(shí)間: 2025-3-27 19:12 作者: effrontery 時(shí)間: 2025-3-28 01:35 作者: 小鹿 時(shí)間: 2025-3-28 02:40 作者: 合法 時(shí)間: 2025-3-28 06:44 作者: 笨拙的你 時(shí)間: 2025-3-28 13:22 作者: rheumatism 時(shí)間: 2025-3-28 16:10
Evolving Robust Solutions in Multi-Objective Optimization tractability, resulting in more uncertainties in the problem model. In addition, it does not allow for the incorporation of any domain knowledge to achieve better performance. On the other hand, evolutionary optimization techniques do not have such limitations, making it appropriate for robust optimization.作者: nitric-oxide 時(shí)間: 2025-3-28 20:29 作者: GREG 時(shí)間: 2025-3-28 23:02
Noisy Evolutionary Multi-objective Optimization external sources, noise can also be intrinsic to the problem. A good example is the evolution of neural networks where the same network structure can give rise to different fitness values due to different weight instantiations [144].作者: 萬(wàn)靈丹 時(shí)間: 2025-3-29 06:10 作者: BUOY 時(shí)間: 2025-3-29 07:46 作者: PAGAN 時(shí)間: 2025-3-29 14:38
1860-949X y algorithms are sophisticated search methods that have been found to be very efficient and effective in solving complex real-world multi-objective problems where conventional optimization tools fail to work well. Despite the tremendous amount of work done in the development of these algorithms in t作者: Celiac-Plexus 時(shí)間: 2025-3-29 15:48
https://doi.org/10.1007/978-3-663-07215-7s of evolution. Given that the intrinsic relationship between the architecture and the associated synaptic weights can be quite complex, the design methodology would be flawed if we were to decouple these two properties during the training phase of the network. The design of ANN has two intrinsical noise sources:作者: 千篇一律 時(shí)間: 2025-3-29 22:54
https://doi.org/10.1007/978-3-658-01120-8n process, which inevitably leads to the inability to track the dynamic Pareto front. Therefore, it is necessary to maintain or generate sufficient diversity to explore the search space when the multi-objective problem changes.
