Landscape analysis of gaussian process surrogates for the covariance matrix adaptation evolution strategy-Reference-Cited by-同舟云学术

Landscape analysis of gaussian process surrogates for the covariance matrix adaptation evolution strategy

Published:2019-07-13 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference
language:
Short-container-title:

Author:

Pitra Zbyněk¹,Repický Jakub²,Holeňa Martin³

Affiliation:

1. Czech Technical University, Prague, Czech Republic

2. Charles University, Prague, Czech Rep.

3. Institute of Computer Science, Prague, Czech Republic

Funder

ðeské Vysoké Uðení Technické v Praze

Grantová Agentura ðeské Republiky

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3321707.3321861

Reference34 articles.

1. A Restart CMA Evolution Strategy With Increasing Population Size

2. A. Auger M. Schoenauer and N. Vanhaecke. 2004. LS-CMA-ES: A Second-Order Algorithm for Covariance Matrix Adaptation. In Parallel Problem Solving from Nature - PPSN VIII. 182--191. A. Auger M. Schoenauer and N. Vanhaecke. 2004. LS-CMA-ES: A Second-Order Algorithm for Covariance Matrix Adaptation. In Parallel Problem Solving from Nature - PPSN VIII. 182--191.

3. M. Baerns and M. Holeňa. 2009. Combinatorial Development of Solid Catalytic Materials. Design of High-Throughput Experiments Data Analysis Data Mining. M. Baerns and M. Holeňa. 2009. Combinatorial Development of Solid Catalytic Materials. Design of High-Throughput Experiments Data Analysis Data Mining.

4. L. Bajer Z. Pitra J. Repický and M. Holeňa. 0. Gaussian Process Surrogate Models for the CMA Evolution Strategy. Evolutionary Computation 0 ja (0) 1--30. PMID: 30540493. L. Bajer Z. Pitra J. Repický and M. Holeňa. 0. Gaussian Process Surrogate Models for the CMA Evolution Strategy. Evolutionary Computation 0 ja (0) 1--30. PMID: 30540493.

5. N. Belkhir J. Dréo P. Savéant and M. Schoenauer. 2016. Surrogate Assisted Feature Computation for Continuous Problems. In Learning and Intelligent Optimization. Springer International Publishing Cham 17--31. N. Belkhir J. Dréo P. Savéant and M. Schoenauer. 2016. Surrogate Assisted Feature Computation for Continuous Problems. In Learning and Intelligent Optimization. Springer International Publishing Cham 17--31.

Cited by 10 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Hybrid CMAES Method with Convex Hull Surrogate Model;2024 IEEE Congress on Evolutionary Computation (CEC);2024-06-30

2. On the Utility of Probing Trajectories for Algorithm-Selection;Lecture Notes in Computer Science;2024

3. Exploratory Landscape Analysis;Proceedings of the Companion Conference on Genetic and Evolutionary Computation;2023-07-15

4. Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games;Applied Soft Computing;2023-03

5. A Survey of Nearest-Better Clustering in Swarm and Evolutionary Computation;2021 IEEE Congress on Evolutionary Computation (CEC);2021-06-28