使激动
发表于 2025-3-25 05:53:19
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.
ANTI
发表于 2025-3-25 11:08:16
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Ganglion-Cyst
发表于 2025-3-25 15:09:37
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迅速成长
发表于 2025-3-25 19:51:35
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.
Assemble
发表于 2025-3-25 21:27:29
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.
Constrain
发表于 2025-3-26 01:12:53
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斗志
发表于 2025-3-26 07:19:32
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.
palette
发表于 2025-3-26 12:05:19
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.
外向者
发表于 2025-3-26 16:02:10
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.
nonsensical
发表于 2025-3-26 18:38:47
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.