標題: Titlebook: Evolutionary Learning: Advances in Theories and Algorithms; Zhi-Hua Zhou,Yang Yu,Chao Qian Book 2019 Springer Nature Singapore Pte Ltd. 20 [打印本頁] 作者: ARGOT 時間: 2025-3-21 17:24
書目名稱Evolutionary Learning: Advances in Theories and Algorithms影響因子(影響力)
書目名稱Evolutionary Learning: Advances in Theories and Algorithms影響因子(影響力)學科排名
書目名稱Evolutionary Learning: Advances in Theories and Algorithms網(wǎng)絡(luò)公開度
書目名稱Evolutionary Learning: Advances in Theories and Algorithms網(wǎng)絡(luò)公開度學科排名
書目名稱Evolutionary Learning: Advances in Theories and Algorithms被引頻次
書目名稱Evolutionary Learning: Advances in Theories and Algorithms被引頻次學科排名
書目名稱Evolutionary Learning: Advances in Theories and Algorithms年度引用
書目名稱Evolutionary Learning: Advances in Theories and Algorithms年度引用學科排名
書目名稱Evolutionary Learning: Advances in Theories and Algorithms讀者反饋
書目名稱Evolutionary Learning: Advances in Theories and Algorithms讀者反饋學科排名
作者: 牛的細微差別 時間: 2025-3-21 20:22
Existence: Semantics and Syntaxhe original constrained optimization problem into a bi-objective optimization problem, is probably better than the commonly employed penalty method and the greedy method. Its effectiveness is moreover verified in machine learning tasks.作者: 廢除 時間: 2025-3-22 03:58 作者: Inoperable 時間: 2025-3-22 05:52
Constrained Optimizationhe original constrained optimization problem into a bi-objective optimization problem, is probably better than the commonly employed penalty method and the greedy method. Its effectiveness is moreover verified in machine learning tasks.作者: CLAY 時間: 2025-3-22 12:45 作者: 兵團 時間: 2025-3-22 15:45 作者: 兵團 時間: 2025-3-22 20:45 作者: 畏縮 時間: 2025-3-22 23:00 作者: chandel 時間: 2025-3-23 03:25 作者: engender 時間: 2025-3-23 08:47
https://doi.org/10.1007/978-94-007-4207-9helpful, while for easy problems, it can be harmful. The findings are verified in the experiments. We also prove that the two common strategies, i.e., threshold selection and sampling, can bring robustness against noise when it is harmful.作者: negligence 時間: 2025-3-23 12:49 作者: Root494 時間: 2025-3-23 16:36
Genevieve A. Dingle,Leah S. Sharmanl learners. We show that a Pareto optimization algorithm, POSE, solves the learning problem better than previous ordering-based selective ensemble methods as well as the heuristic single-objective optimization-based methods, supported by theoretical analysis and experiment results.作者: 善辯 時間: 2025-3-23 21:12
Joseph C. Schmid,Daniel J. Linfordd on Pareto optimization, we present the PO.SS algorithm for the problem, which is proven to have the state-of-the-art performance and is verified empirically on the applications of influence maximization, information coverage maximization, and sensor placement experiments.作者: 歌曲 時間: 2025-3-23 22:49
Zhi-Hua Zhou,Yang Yu,Chao QianPresents theoretical results for evolutionary learning.Provides general theoretical tools for analysing evolutionary algorithms.Proposes evolutionary learning algorithms with provable theoretical guar作者: 笨拙處理 時間: 2025-3-24 04:30
http://image.papertrans.cn/e/image/317970.jpg作者: dominant 時間: 2025-3-24 07:12 作者: sebaceous-gland 時間: 2025-3-24 13:48
https://doi.org/10.1007/BFb0073401rom bridging two fundamental theoretical issues. The approach is applied to show the exponential lower bound of the expected running time for (1+1)-EA and randomized local search solving the constrained Trap problem.作者: 引起 時間: 2025-3-24 16:27 作者: Mingle 時間: 2025-3-24 19:52 作者: absolve 時間: 2025-3-25 00:36
Wolfgang Spohn,Bas C. Fraassen,Brian Skyrmscompetition among solutions and offers a general characterization of approximation behaviors. The framework is applied to the set cover problem, delivering an .-approximation ratio that matches the asymptotic lower bound.作者: 使激動 時間: 2025-3-25 05:53
https://doi.org/10.1007/978-3-540-72691-3gorithm. Through the derived theorem, the easiest and hardest functions in the pseudo-Boolean function class with a unique global optimal solution are identified for (1+1)-EA with any mutation probability less than 0.5.作者: ANTI 時間: 2025-3-25 11:08 作者: Ganglion-Cyst 時間: 2025-3-25 15:09 作者: 迅速成長 時間: 2025-3-25 19:51
Joseph C. Schmid,Daniel J. Linfordd on Pareto optimization, we present the PO.SS algorithm for the problem, which is proven to have the state-of-the-art performance and is verified empirically on the applications of influence maximization, information coverage maximization, and sensor placement experiments.作者: Assemble 時間: 2025-3-25 21:27
Running Time Analysis: Convergence-based Analysisrom bridging two fundamental theoretical issues. The approach is applied to show the exponential lower bound of the expected running time for (1+1)-EA and randomized local search solving the constrained Trap problem.作者: Constrain 時間: 2025-3-26 01:12 作者: 斗志 時間: 2025-3-26 07:19
Running Time Analysis: Comparison and Unificationreducibility relation between two approaches. Consequently, we find that switch analysis can serve as a unified analysis approach, as other approaches can be reduced to switch analysis. This unification also provides a perspective to understand different approaches.作者: palette 時間: 2025-3-26 12:05
Approximation Analysis: SEIPcompetition among solutions and offers a general characterization of approximation behaviors. The framework is applied to the set cover problem, delivering an .-approximation ratio that matches the asymptotic lower bound.作者: 外向者 時間: 2025-3-26 16:02
Boundary Problems of EAsgorithm. Through the derived theorem, the easiest and hardest functions in the pseudo-Boolean function class with a unique global optimal solution are identified for (1+1)-EA with any mutation probability less than 0.5.作者: nonsensical 時間: 2025-3-26 18:38
Inaccurate Fitness Evaluationhelpful, while for easy problems, it can be harmful. The findings are verified in the experiments. We also prove that the two common strategies, i.e., threshold selection and sampling, can bring robustness against noise when it is harmful.作者: 北京人起源 時間: 2025-3-26 21:57 作者: exquisite 時間: 2025-3-27 03:20 作者: 傾聽 時間: 2025-3-27 05:42
https://doi.org/10.1007/978-981-13-5956-9Artificial intelligence; Machine Learning; Evolutionary Learning; Evolutionary Algorithms; Evolutionary 作者: 怪物 時間: 2025-3-27 10:14
The Surge: Exile and Crime in Siberia,This chapter briefly introduces basic concepts including machine learning, evolutionary learning, multi-objective optimization, as well as the organization of the book.作者: overrule 時間: 2025-3-27 14:21 作者: slow-wave-sleep 時間: 2025-3-27 18:21 作者: Capture 時間: 2025-3-28 01:49 作者: burnish 時間: 2025-3-28 03:01
Existence, Historical Fabulation, DestinyThis chapter studies the influence of population on evolutionary algorithms. We show that, on one hand, population is unexpected for simple functions such as OneMax and LeadningOnes by derving the lower running time bound, and on the other hand, in the presence of noise, using population can enhance the robustness against noise.作者: 反對 時間: 2025-3-28 09:00 作者: 讓步 時間: 2025-3-28 14:00 作者: 冒號 時間: 2025-3-28 18:19
PreliminariesThis chapter introduces preliminaries. Including basic evolutionary algorithms, pseudo-Boolean functions for theoretical studies, and basic knowledge for analyzing running time complexity of evolutionary algorithms.作者: 案發(fā)地點 時間: 2025-3-28 22:10
RecombinationThis chapter studies the influence of recombination operators. We show that, in multi-objective evolutionary optimization, recombination operators are useful for multi-objective evolutionary optimization by accelerating the filling of the Pareto front. This principle may also hold in more situations.作者: 免除責任 時間: 2025-3-28 22:56 作者: 小臼 時間: 2025-3-29 03:51
PopulationThis chapter studies the influence of population on evolutionary algorithms. We show that, on one hand, population is unexpected for simple functions such as OneMax and LeadningOnes by derving the lower running time bound, and on the other hand, in the presence of noise, using population can enhance the robustness against noise.作者: 拋媚眼 時間: 2025-3-29 07:45 作者: 職業(yè)拳擊手 時間: 2025-3-29 11:40 作者: Hemiparesis 時間: 2025-3-29 17:01 作者: Thyroid-Gland 時間: 2025-3-29 22:51 作者: MIRTH 時間: 2025-3-30 03:23
s for the analysis of running time and approximation performance in evolutionary algorithms. Based on these general tools, Part III presents a number of theoretical findings on major factors in evolutionary opt978-981-13-5956-9作者: COST 時間: 2025-3-30 05:21
Running Time Analysis: Convergence-based Analysisrom bridging two fundamental theoretical issues. The approach is applied to show the exponential lower bound of the expected running time for (1+1)-EA and randomized local search solving the constrained Trap problem.作者: refraction 時間: 2025-3-30 09:52 作者: ANN 時間: 2025-3-30 13:27 作者: Digitalis 時間: 2025-3-30 17:10
Approximation Analysis: SEIPcompetition among solutions and offers a general characterization of approximation behaviors. The framework is applied to the set cover problem, delivering an .-approximation ratio that matches the asymptotic lower bound.作者: 強化 時間: 2025-3-31 00:38
Boundary Problems of EAsgorithm. Through the derived theorem, the easiest and hardest functions in the pseudo-Boolean function class with a unique global optimal solution are identified for (1+1)-EA with any mutation probability less than 0.5.作者: Gratuitous 時間: 2025-3-31 03:43
