標(biāo)題: Titlebook: Computational Intelligence in Expensive Optimization Problems; Yoel Tenne,Chi-Keong Goh Book 2010 Springer-Verlag Berlin Heidelberg 2010 a [打印本頁(yè)] 作者: 側(cè)面上下 時(shí)間: 2025-3-21 18:07
書(shū)目名稱Computational Intelligence in Expensive Optimization Problems影響因子(影響力)
書(shū)目名稱Computational Intelligence in Expensive Optimization Problems影響因子(影響力)學(xué)科排名
書(shū)目名稱Computational Intelligence in Expensive Optimization Problems網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Computational Intelligence in Expensive Optimization Problems網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Computational Intelligence in Expensive Optimization Problems被引頻次
書(shū)目名稱Computational Intelligence in Expensive Optimization Problems被引頻次學(xué)科排名
書(shū)目名稱Computational Intelligence in Expensive Optimization Problems年度引用
書(shū)目名稱Computational Intelligence in Expensive Optimization Problems年度引用學(xué)科排名
書(shū)目名稱Computational Intelligence in Expensive Optimization Problems讀者反饋
書(shū)目名稱Computational Intelligence in Expensive Optimization Problems讀者反饋學(xué)科排名
作者: 歪曲道理 時(shí)間: 2025-3-21 20:52 作者: Obverse 時(shí)間: 2025-3-22 03:30
,Die Eind?mmung der Gewalt im Gro?raum,ution of computationally expensive optimization problems. Existing methods developed by other researchers or the authors’ group are overviewed and a new enhancement based on fitness inheritance is proposed. Whereas conventional evolutionary algorithms require a great number of calls to the evaluatio作者: 截?cái)?nbsp; 時(shí)間: 2025-3-22 05:57 作者: Canvas 時(shí)間: 2025-3-22 11:19
Politik und globale Machtprojektion, reduce the number of function evaluations effectively. Although the approximation errors between the true function values and the approximation values are not small, the rough model can estimate the order relation of solutions with fair accuracy. By utilizing this nature of the rough model, we have作者: NIL 時(shí)間: 2025-3-22 16:48
Schriften zur Unternehmensentwicklung algorithms used in the field of computer experiments are based on Kriging (Gaussian process regression). Starting with a spatial predictor including a measure of uncertainty, they proceed by iteratively choosing the point maximizing a criterion which is a compromise between predicted performance an作者: NIL 時(shí)間: 2025-3-22 19:04 作者: 一加就噴出 時(shí)間: 2025-3-22 21:34
https://doi.org/10.1007/978-3-322-90403-4e help of modern statistical techniques to create powerful strategies for expensive optimization. For example, it shows how the regularization of some parameters of the EDAs probabilistic models can yield dramatic improvements in efficiency. In this context a new class, Shrinkage EDAs, based on shri作者: fibula 時(shí)間: 2025-3-23 04:28 作者: Gleason-score 時(shí)間: 2025-3-23 07:06 作者: Isometric 時(shí)間: 2025-3-23 10:17
Strategisches Controlling und Strukturation regression. A least-squares regression is used to estimate the shape of the local fitness landscape. From this shape, the expected location of the peak is calculated and the information given to the PSO. By guiding the PSO to the optimum, the local convergence speed can be vastly improved. We demon作者: 體貼 時(shí)間: 2025-3-23 17:19
https://doi.org/10.1007/978-3-322-90403-4tial Evolution for Large Scale problems (DELS) is a Differential Evolution (DE) based Memetic Algorithm with self-adaptive control parameters and automatic population size reduction, which employs within its framework a variation operator local search. The local search algorithm is applied to the sc作者: 硬化 時(shí)間: 2025-3-23 18:29
Strategie und Technik des Automobilmarketingration tasks involve difficult decision-making processes that can be formulated as optimization problems: large-scale combinatorial optimization problems. These problems have been addressed successfully with specially designed evolutionary hybrid approaches. Such approaches rely upon Lamarckian evol作者: CLOUT 時(shí)間: 2025-3-23 22:49
Wachstumsstrategien in neuen M?rktenthods use a non-deterministic approach that finds good solutions, despite not ensuring the determination of the overall optimum. The success of a metaheuristic is conditioned on its capacity of alternating properly between the exploration and exploitation of solution spaces. During the process of se作者: Medicare 時(shí)間: 2025-3-24 05:45 作者: 有其法作用 時(shí)間: 2025-3-24 09:51
Die Beziehung zum Kunden gestaltenhe TS type with different trade-offs between, generally, complexity/interpretability and accuracy. The application of these algorithms requires a large number of TS system generations and evaluations.When we deal with high dimensional data sets, these tasks can be very time-consuming, thus making an作者: Pageant 時(shí)間: 2025-3-24 12:54
Die Kommunikation orchestrieren multi-criterion minimum spanning tree (MCMST) model, a combinatorial optimization problem that has been shown to be NP-Hard. In Pareto Optimization of the model no polynomial time algorithm is known to find the Pareto front for all instances of the MCMST problem. Researchers have therefore develope作者: 集合 時(shí)間: 2025-3-24 18:52 作者: Generosity 時(shí)間: 2025-3-24 20:51 作者: CARE 時(shí)間: 2025-3-25 00:29
Computational Intelligence in Expensive Optimization Problems978-3-642-10701-6Series ISSN 1867-4534 Series E-ISSN 1867-4542 作者: anatomical 時(shí)間: 2025-3-25 07:04
https://doi.org/10.1007/978-3-642-10701-6algorithm; algorithms; computational intelligence; control; data mining; evolution; evolutionary algorithm作者: 叢林 時(shí)間: 2025-3-25 07:35
978-3-642-26318-7Springer-Verlag Berlin Heidelberg 2010作者: Mucosa 時(shí)間: 2025-3-25 14:09 作者: 指令 時(shí)間: 2025-3-25 15:50
1867-4534 tness inheritance), frameworks for optimization (model management, complexity control, model selection), parallelization of algorithms (implementation issues on clusters, grids, parallel machines), incorporatio978-3-642-26318-7978-3-642-10701-6Series ISSN 1867-4534 Series E-ISSN 1867-4542 作者: 粗俗人 時(shí)間: 2025-3-25 23:11
A Survey of Fitness Approximation Methods Applied in Evolutionary Algorithmsresented. The main focus areas are the methods of fitness approximation, the working styles of fitness approximation, and the management of the approximation during the optimization process. To conclude, some open questions in this area are discussed.作者: 彎曲道理 時(shí)間: 2025-3-26 02:39 作者: etiquette 時(shí)間: 2025-3-26 04:38
Multilevel Optimization Algorithms Based on Metamodel- and Fitness Inheritance-Assisted Evolutionaryalues in place of metamodels. In addition, to profit of the availability of evaluation or parameterization models of lower fidelity and . cost and/or refinement methods, a multilevel search algorithm relying also on the use of metamodels is presented. The algorithm may optionally operate as hierarch作者: Keratectomy 時(shí)間: 2025-3-26 09:22
Knowledge-Based Variable-Fidelity Optimization of Expensive Objective Functions through Space Mappintrate both input space mapping and implicit space mapping through the space mapping optimization of a simple, technology-free wedgecutting problem. We also present tuning space mapping, a powerful methodology, but one that requires extra engineering knowledge of the problem under investigation. To c作者: CYN 時(shí)間: 2025-3-26 14:06
Reducing Function Evaluations Using Adaptively Controlled Differential Evolution with Rough Approxime margin parameter and the congestion parameter according to the success rate of the judgment. The advantage of these improvements is shown by comparing the results obtained by Differential Evolution (DE), DE with the estimated comparison method, adaptively controlled DE with the estimated compariso作者: expunge 時(shí)間: 2025-3-26 18:22
Kriging Is Well-Suited to Parallelize Optimizationely optimize the ., and apply them to the classical Branin-Hoo test-case function. It is finally demonstrated within the covered example that the latter strategies perform as good as the best Latin Hypercubes and Uniform Designs ever found by simulation (2000 designs drawn at random for every .?∈ [1作者: 制定法律 時(shí)間: 2025-3-27 00:15
Analysis of Approximation-Based Memetic Algorithms for Engineering Optimizationwith higher precision. The chapter proceeds to the formal analysis of approximation-based memetic algorithms, in which we investigate the effect of the local search operators on the global convergence properties of evolutionary algorithms viaMarkov chain theory, and also study the computational comp作者: 使無(wú)效 時(shí)間: 2025-3-27 01:54 作者: AMPLE 時(shí)間: 2025-3-27 06:59
Multi-objective Model Predictive Control Using Computational Intelligencems, the model predictive control has been developed along a similar idea to the above. This chapter discusses multi-objective model predictive control problems and proposes a method using computational intelligence such as support vector regression.作者: Pituitary-Gland 時(shí)間: 2025-3-27 09:29
Differential Evolution with Scale Factor Local Search for Large Scale Problemsust algorithm for highly multivariate optimization, and the employment of the local search to the scale factor is beneficial in order to detect solutions with a high quality, convergence speed and algorithmic robustness.作者: fringe 時(shí)間: 2025-3-27 15:12 作者: Myosin 時(shí)間: 2025-3-27 21:15 作者: Mobile 時(shí)間: 2025-3-28 01:52 作者: acetylcholine 時(shí)間: 2025-3-28 04:48 作者: 晚來(lái)的提名 時(shí)間: 2025-3-28 06:32
Der Primat der Macht und der Ausnahmefall,nalyze different proposals currently available in the specialized literature to deal with expensive functions in evolutionary multi-objective optimization. Additionally, we review some real-world applications of these methods, which can be seen as case studies in which such techniques led to a subst作者: SAGE 時(shí)間: 2025-3-28 10:59
,Die Eind?mmung der Gewalt im Gro?raum,alues in place of metamodels. In addition, to profit of the availability of evaluation or parameterization models of lower fidelity and . cost and/or refinement methods, a multilevel search algorithm relying also on the use of metamodels is presented. The algorithm may optionally operate as hierarch作者: audiologist 時(shí)間: 2025-3-28 15:48 作者: Enervate 時(shí)間: 2025-3-28 20:06 作者: 羽毛長(zhǎng)成 時(shí)間: 2025-3-28 23:27 作者: Suggestions 時(shí)間: 2025-3-29 06:08
Schriften zur Unternehmensentwicklungwith higher precision. The chapter proceeds to the formal analysis of approximation-based memetic algorithms, in which we investigate the effect of the local search operators on the global convergence properties of evolutionary algorithms viaMarkov chain theory, and also study the computational comp作者: 清澈 時(shí)間: 2025-3-29 07:57 作者: MAIM 時(shí)間: 2025-3-29 13:25
Strategisches Management, Recht und Politikms, the model predictive control has been developed along a similar idea to the above. This chapter discusses multi-objective model predictive control problems and proposes a method using computational intelligence such as support vector regression.作者: 辮子帶來(lái)幫助 時(shí)間: 2025-3-29 18:47 作者: ingrate 時(shí)間: 2025-3-29 20:24 作者: 即席 時(shí)間: 2025-3-30 03:01 作者: 拉開(kāi)這車床 時(shí)間: 2025-3-30 05:57 作者: ETCH 時(shí)間: 2025-3-30 09:39
Book 2010orks and fuzzy logic. reduction of expensive evaluations (modelling, variable-fidelity, fitness inheritance), frameworks for optimization (model management, complexity control, model selection), parallelization of algorithms (implementation issues on clusters, grids, parallel machines), incorporatio作者: 煩憂 時(shí)間: 2025-3-30 16:02
Strategie und Technik des Automobilmarketingutionary hybrid algorithms. In this chapter, we present the most successful implementations of such algorithms and discuss such implementations based on our experience in the development of industrial applications for planning and operation of electric power distribution networks for a period of over ten years.作者: 尖 時(shí)間: 2025-3-30 17:17
Large-Scale Network Optimization with Evolutionary Hybrid Algorithms: Ten Years’ Experience with theutionary hybrid algorithms. In this chapter, we present the most successful implementations of such algorithms and discuss such implementations based on our experience in the development of industrial applications for planning and operation of electric power distribution networks for a period of over ten years.作者: Vasodilation 時(shí)間: 2025-3-30 22:49
Book 2010ions reduces costs and shortens development times but introduces new challenges to design optimization process. Examples of such challenges include limited computational resource for simulation runs, complicated response surface of the simulation inputs-outputs, and etc...Under such difficulties, cl作者: Crumple 時(shí)間: 2025-3-31 01:53 作者: membrane 時(shí)間: 2025-3-31 09:03 作者: 翻動(dòng) 時(shí)間: 2025-3-31 10:25
Wachstumsstrategien in neuen M?rktensearch trajectories, which act competitively and/or cooperatively. This can be accomplished using parallel processing. Thus, in this paper we propose a hybrid parallel implementation for the GRASP metaheuristics and the genetic al gorithm, using reinforcement learning, applied to the symmetric traveling salesman problem.作者: LEER 時(shí)間: 2025-3-31 15:49 作者: exhilaration 時(shí)間: 2025-3-31 20:48
Improving Local Convergence in Particle Swarms by Fitness Approximation Using Regression multiple local peaks. The combination of this technique and a speciation-based PSO compares favourably to another multi-swarm PSO algorithm that has proven to be working well on the Moving peaks test functions.
