Adaptive Parameter Tuning of Evolutionary Computation Algorithms

被引：0

作者：

Choi, Kwok Pui ^{[1
]}

Lai, Tze Leung ^{[2
]}

Tong, Xin T. ^{[3
]}

Tsang, Ka Wai ^{[4
]}

Wong, Weng Kee ^{[5
]}

Zhang, Hongbao ^{[4
]}

机构：

[1] Natl Univ Singapore, Dept Stat & Data Sci, 6 Sci Dr 2, Singapore 117546, Singapore

[2] Stanford Univ, Dept Stat, 390 Jane Stanford Way, Stanford, CA 94305 USA

[3] Natl Univ Singapore, Dept Math, 10 Lower Kent Ridge Rd, Singapore 119076, Singapore

[4] Chinese Univ Hong Kong, Sch Data Sci, 2001 Longxiang Blvd, Shenzhen 518172, Longgang, Peoples R China

[5] Univ Calif Los Angeles, Dept Biostat, 650 Charles Young Dr, Los Angeles, CA 90095 USA

来源：

STATISTICS IN BIOSCIENCES | 2025年

关键词：

& varepsilon; -Greedy randomization; Adaptive arm elimination; Contextual multi-armed bandits; Decoupling inequalities; Hybrid resampling; NATURE-INSPIRED OPTIMIZATION; PARTICLE SWARM OPTIMIZATION; OPTIMAL DESIGNS; ALLOCATION; BANDITS;

D O I：

10.1007/s12561-025-09500-w

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Herein, we consider the long-standing problem of adaptive parameter tuning and propose a novel approach, with optimal properties that achieve oracle bounds, to meet the challenges in new important applications in the big-data multi-cloud era.

引用

页数：31

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