negligence 发表于 2025-3-23 12:49:59

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Root494 发表于 2025-3-23 16:36:07

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.

善辩 发表于 2025-3-23 21:12:54

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.

歌曲 发表于 2025-3-23 22:49:30

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

笨拙处理 发表于 2025-3-24 04:30:40

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dominant 发表于 2025-3-24 07:12:34

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sebaceous-gland 发表于 2025-3-24 13:48:43

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.

引起 发表于 2025-3-24 16:27:53

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Mingle 发表于 2025-3-24 19:52:59

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absolve 发表于 2025-3-25 00:36:19

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.
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查看完整版本: Titlebook: Evolutionary Learning: Advances in Theories and Algorithms; Zhi-Hua Zhou,Yang Yu,Chao Qian Book 2019 Springer Nature Singapore Pte Ltd. 20