標(biāo)題: Titlebook: Computational Intelligence in Optimization; Applications and Imp Yoel Tenne,Chi-Keong Goh Book 2010 Springer-Verlag Berlin Heidelberg 2010 [打印本頁] 作者: 孵化 時(shí)間: 2025-3-21 18:24
書目名稱Computational Intelligence in Optimization影響因子(影響力)
書目名稱Computational Intelligence in Optimization影響因子(影響力)學(xué)科排名
書目名稱Computational Intelligence in Optimization網(wǎng)絡(luò)公開度
書目名稱Computational Intelligence in Optimization網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Computational Intelligence in Optimization被引頻次
書目名稱Computational Intelligence in Optimization被引頻次學(xué)科排名
書目名稱Computational Intelligence in Optimization年度引用
書目名稱Computational Intelligence in Optimization年度引用學(xué)科排名
書目名稱Computational Intelligence in Optimization讀者反饋
書目名稱Computational Intelligence in Optimization讀者反饋學(xué)科排名
作者: 賞心悅目 時(shí)間: 2025-3-21 22:22
978-3-642-26361-3Springer-Verlag Berlin Heidelberg 2010作者: menopause 時(shí)間: 2025-3-22 01:43 作者: 夾克怕包裹 時(shí)間: 2025-3-22 07:02
https://doi.org/10.1007/978-3-030-74458-8he word; complexity addresses, amongst others, non-linear, contingent and ‘chaotic’ phenomena ([10],[11]). Many thinkers on complexity consider such characteristics - sometimes called .-to demarcate a transition point where analytical approaches are no longer feasible ([26]:18).作者: 儀式 時(shí)間: 2025-3-22 10:33 作者: subordinate 時(shí)間: 2025-3-22 12:52 作者: subordinate 時(shí)間: 2025-3-22 17:25 作者: Sad570 時(shí)間: 2025-3-22 23:24
Strategies for Second Language Listeningtimization tools for solving large scale problems was due to the fact that this technique has great potential for hardware VLSI implementation, in which it may be more efficient than traditional optimization techniques. However, the implementation of computational algorithm has shown that the propos作者: 輕而薄 時(shí)間: 2025-3-23 04:11 作者: 吞噬 時(shí)間: 2025-3-23 06:56
https://doi.org/10.1007/978-3-031-04174-7lation-based search. We provide motivation and comparison to similar, but different approaches including antithetic variates and quasi-randomness/low-discrepancy sequences. We employ differential evolution and population-based incremental learning as optimization methods for image thresholding. Our 作者: STYX 時(shí)間: 2025-3-23 13:12
Stephanie Mckendry,Matson Lawrencee function evaluation is expensive. It was introduced in [1] and further developed in [2]. In this paper we present the convergence theorem of the method. The theorem is proved for the EXTREM [6] algorithm but applies to any Gauss-Siedle algorithm that uses sequential quadratic interpolation (SQI) a作者: 收集 時(shí)間: 2025-3-23 14:44
Refugees in Neoliberal Universitiesconstraints. Exact methods, such as branch-and-bound, require lengthy computations and are, for this reason, infeasible in practice. As an alternative, this study focuses on approximate techniques that can identify near-optimal solutions at a reduced computational cost. Most of the methods considere作者: linear 時(shí)間: 2025-3-23 18:48
Antoinette Geagea,Judith MacCalluml or experimental expense involved. Practical multiobjective optimization was considered almost as a utopia even in academic studies due to the multiplication of this expense. This paper discusses the idea of using surrogate models for multiobjective optimization. With recent advances in grid and pa作者: 無情 時(shí)間: 2025-3-24 00:38
Stephanie Mckendry,Matson Lawrence algorithms allow for existing many species and sexes of agents within the system as well as for defining co-evolutionary interactions between species and sexes. Algorithms based on the model of co-evolutionary multi-agent system have been already applied in many domains, like multi-modal optimizati作者: 咒語 時(shí)間: 2025-3-24 02:32
https://doi.org/10.1007/978-3-030-43593-6lti-agent framework in which a number of existing algorithms, cast as agents, are deployed with the aim to solve the problem in hand as efficiently as possible. The key factor for the success of this approach is a dynamic resource allocation biased toward promising algorithms on the given problem. T作者: 機(jī)構(gòu) 時(shí)間: 2025-3-24 10:07 作者: constitute 時(shí)間: 2025-3-24 14:11
Refugees in Neoliberal Universitiesn using the compressed data in actual classification process which is based on differential evolution algorithm, an evolutionary optimization algorithm. This method is applied here for prediction diagnosis from clinical data sets with chief complaint of chest pain using classical Electronic Medical 作者: 凝乳 時(shí)間: 2025-3-24 15:10
Peter A. Wilderer,Michael von Hauff-SVM are desirable in filtering out the irrelevant features and thus improve the accuracy; the selection itself might also offer critical insights into the problems. However, the high computational cost greatly discourages the application of GA-SVM, especially for large-scale datasets. In this paper作者: MULTI 時(shí)間: 2025-3-24 21:16
https://doi.org/10.1007/978-3-030-74458-8he word; complexity addresses, amongst others, non-linear, contingent and ‘chaotic’ phenomena ([10],[11]). Many thinkers on complexity consider such characteristics - sometimes called .-to demarcate a transition point where analytical approaches are no longer feasible ([26]:18).作者: 愛管閑事 時(shí)間: 2025-3-24 23:32 作者: 攀登 時(shí)間: 2025-3-25 06:51
https://doi.org/10.1007/978-3-030-74458-8implicity of their processing elements (PEs), modularity of design, regular and nearest neighbor interconnections between the PEs, high-level of pipelinability, small chip-area and low-power consumption. In systolic arrays, the desired data is pumped rhythmically in a regular interval across the PEs作者: 挖掘 時(shí)間: 2025-3-25 07:34 作者: 恃強(qiáng)凌弱的人 時(shí)間: 2025-3-25 12:36
Book 2010on to challenging real-world problems. Topics covered include: Intelligent agent-based algorithms, Hybrid intelligent systems, Cognitive and evolutionary robotics, Knowledge-Based Engineering, fuzzy sets and systems, Bioinformatics and Bioengineering, Computational finance and Computational economic作者: 擦試不掉 時(shí)間: 2025-3-25 19:12 作者: 厭煩 時(shí)間: 2025-3-25 23:57 作者: 無聊點(diǎn)好 時(shí)間: 2025-3-26 00:18
Refugees in Neoliberal Universitiestimization problems of practical interest that arise in the fields of machine learning (pruning of ensembles of classifiers), quantitative finance (portfolio selection), time-series modeling (index tracking) and statistical data analysis (sparse principal component analysis).作者: Processes 時(shí)間: 2025-3-26 05:22
Antoinette Geagea,Judith MacCallumace methods on a pre-selected set of test functions. We aim to show that there are number of techniques which can be used to tackle difficult problems and we also demonstrate that a careful choice of response surface methods is important when carrying out surrogate assisted multiobjective search.作者: 會(huì)議 時(shí)間: 2025-3-26 09:37
Peter A. Wilderer,Michael von Hauffzation, 2) SVM Parallelization, 3) Neighbor Search and 4) Evaluation Caching. All the four strategies improve the respective aspects of the feature selection algorithm and contribute collectively towards higher computational throughput.作者: OVERT 時(shí)間: 2025-3-26 16:39 作者: Substitution 時(shí)間: 2025-3-26 19:59
Multi-Objective Optimization Using Surrogates,ace methods on a pre-selected set of test functions. We aim to show that there are number of techniques which can be used to tackle difficult problems and we also demonstrate that a careful choice of response surface methods is important when carrying out surrogate assisted multiobjective search.作者: 善變 時(shí)間: 2025-3-27 00:35 作者: 燈絲 時(shí)間: 2025-3-27 02:48
Stephanie Mckendry,Matson Lawrenceosed transformations. This method of the proof was chosen instead of sufficient decrease approach since the crucial element of the presented proof is an extension of the SQI convergence proof from [14] which is based on this approach.作者: 明確 時(shí)間: 2025-3-27 08:15 作者: single 時(shí)間: 2025-3-27 13:17
Peter A. Wilderer,Michael von Hauffrom precedence constraints, and (iii) project uncertainties. We also present a hybrid meta heuristic (HMH) combining a genetic algorithm with simulated annealing to solve discrete version of multiobjective TCT problem. HMH is employed to solve two test cases of TCT.作者: Mere僅僅 時(shí)間: 2025-3-27 17:24
1867-4534 real-world insights gained by experience in computational in.This volume presents a collection of recent studies covering the spectrum of computational intelligence applications with emphasis on their application to challenging real-world problems. Topics covered include: Intelligent agent-based alg作者: rectocele 時(shí)間: 2025-3-27 18:41 作者: transdermal 時(shí)間: 2025-3-28 00:28 作者: 合同 時(shí)間: 2025-3-28 03:20 作者: 無底 時(shí)間: 2025-3-28 09:26
A Novel Optimization Algorithm Based on Reinforcement Learning,t learning principle to determine the particle move in search for the optimum process. A model of successful actions is build and future actions are based on past experience. The step increment combines exploitation of the known search path and exploration for the improved search direction. The algo作者: ingrate 時(shí)間: 2025-3-28 13:27 作者: 十字架 時(shí)間: 2025-3-28 16:50 作者: pulmonary 時(shí)間: 2025-3-28 22:47 作者: Favorable 時(shí)間: 2025-3-29 02:55
Multi-Objective Optimization Using Surrogates,l or experimental expense involved. Practical multiobjective optimization was considered almost as a utopia even in academic studies due to the multiplication of this expense. This paper discusses the idea of using surrogate models for multiobjective optimization. With recent advances in grid and pa作者: 勉勵(lì) 時(shí)間: 2025-3-29 06:11
A Review of Agent-Based Co-Evolutionary Algorithms for Multi-Objective Optimization, algorithms allow for existing many species and sexes of agents within the system as well as for defining co-evolutionary interactions between species and sexes. Algorithms based on the model of co-evolutionary multi-agent system have been already applied in many domains, like multi-modal optimizati作者: Feedback 時(shí)間: 2025-3-29 10:37
A Game Theory-Based Multi-Agent System for Expensive Optimisation Problems,lti-agent framework in which a number of existing algorithms, cast as agents, are deployed with the aim to solve the problem in hand as efficiently as possible. The key factor for the success of this approach is a dynamic resource allocation biased toward promising algorithms on the given problem. T作者: 朝圣者 時(shí)間: 2025-3-29 15:05 作者: 寒冷 時(shí)間: 2025-3-29 18:58 作者: MINT 時(shí)間: 2025-3-29 23:22
An Integrated Approach to Speed Up GA-SVM Feature Selection Model,-SVM are desirable in filtering out the irrelevant features and thus improve the accuracy; the selection itself might also offer critical insights into the problems. However, the high computational cost greatly discourages the application of GA-SVM, especially for large-scale datasets. In this paper作者: 放牧 時(shí)間: 2025-3-30 01:53
Computation in Complex Environments;,he word; complexity addresses, amongst others, non-linear, contingent and ‘chaotic’ phenomena ([10],[11]). Many thinkers on complexity consider such characteristics - sometimes called .-to demarcate a transition point where analytical approaches are no longer feasible ([26]:18).作者: GOAT 時(shí)間: 2025-3-30 07:02 作者: 沒收 時(shí)間: 2025-3-30 08:27 作者: Outwit 時(shí)間: 2025-3-30 15:47 作者: cardiopulmonary 時(shí)間: 2025-3-30 17:11 作者: 啟發(fā) 時(shí)間: 2025-3-30 21:06
A Novel Optimization Algorithm Based on Reinforcement Learning,zation problem. The optimized multi-layer perceptron was applied to Iris database classification. Finally, the algorithm is used in image recognition to find a familiar object with retina sampling and micro-saccades.作者: 欺騙手段 時(shí)間: 2025-3-31 02:05
A Review of Agent-Based Co-Evolutionary Algorithms for Multi-Objective Optimization,g solved, agent-based algorithms obtain comparable, and sometimes even better, results than “classical” algorithms, however of course they are not the universal solver for all multi-objective optimization problems.
