Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy

19 June 2020

Papers citing "Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy"

8 / 8 papers shown

Title
Reinforcement learning Based Automated Design of Differential Evolution Algorithm for Black-box Optimization Xu Yang Rui Wang Kaiwen Li Ling Wang 56 0 0 22 Jan 2025
A Survey of Meta-features Used for Automated Selection of Algorithms for Black-box Single-objective Continuous Optimization Gjorgjina Cenikj Ana Nikolikj Gašper Petelin Niki van Stein Carola Doerr T. Eftimov 54 1 0 08 Jun 2024
Challenges of ELA-guided Function Evolution using Genetic Programming Fu Xing Long Diederick Vermetten Anna V. Kononova Roman Kalkreuth Kaifeng Yang Thomas Bäck Niki van Stein 22 6 0 24 May 2023
Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms Ana Kostovska Diederick Vermetten S. Džeroski P. Panov T. Eftimov Carola Doerr 12 8 0 24 Jan 2023
BBOB Instance Analysis: Landscape Properties and Algorithm Performance across Problem Instances F. Long Diederick Vermetten Bas van Stein Anna V. Kononova 16 19 0 29 Nov 2022
OPTION: OPTImization Algorithm Benchmarking ONtology Ana Kostovska Diederick Vermetten Carola Doerr S. Džeroski P. Panov T. Eftimov 21 11 0 21 Nov 2022
The Importance of Landscape Features for Performance Prediction of Modular CMA-ES Variants Ana Kostovska Diederick Vermetten S. Džeroski Carola Doerr Peter Korošec T. Eftimov 31 8 0 15 Apr 2022
Towards Feature-Based Performance Regression Using Trajectory Data Anja Jankovic T. Eftimov Carola Doerr 28 26 0 10 Feb 2021