A collection of deep learning-based feature-free approaches for characterizing single-objective continuous fitness landscapes MV Seiler, RP Prager, P Kerschke, H Trautmann
Proceedings of the Genetic and Evolutionary Computation Conference, 657-665, 2022
14 2022 Nullifying the inherent bias of non-invariant exploratory landscape analysis features RP Prager, H Trautmann
International Conference on the Applications of Evolutionary Computation …, 2023
12 2023 Per-instance configuration of the modularized CMA-ES by means of classifier chains and exploratory landscape analysis RP Prager, H Trautmann, H Wang, THW Bäck, P Kerschke
2020 IEEE Symposium Series on Computational Intelligence (SSCI), 996-1003, 2020
12 2020 Automated algorithm selection in single-objective continuous optimization: A comparative study of deep learning and landscape analysis methods RP Prager, MV Seiler, H Trautmann, P Kerschke
International Conference on Parallel Problem Solving from Nature, 3-17, 2022
11 2022 HPO L Schneider, L Schäpermeier, RP Prager, B Bischl, H Trautmann, ...
International Conference on Parallel Problem Solving from Nature, 575-589, 2022
10 2022 Towards feature-free automated algorithm selection for single-objective continuous black-box optimization RP Prager, MV Seiler, H Trautmann, P Kerschke
2021 IEEE Symposium Series on Computational Intelligence (SSCI), 1-8, 2021
10 2021 Pflacco: Feature-based landscape analysis of continuous and constrained optimization problems in Python RP Prager, H Trautmann
Evolutionary Computation, 1-6, 2024
9 2024 An Experiment on Game Facet Combination\ RP Prager, L Troost, S Brüggenjürgen, D Melhart, G Yannakakis, ...
2019 IEEE Conference on Games (CoG), 1-8, 2019
4 2019 Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems RP Prager, H Trautmann
Proceedings of the Companion Conference on Genetic and Evolutionary …, 2023
2 2023 Exploratory Landscape Analysis for Mixed-Variable Problems RP Prager, H Trautmann
arXiv preprint arXiv:2402.16467, 2024
1 2024 Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features RP Prager, K Dietrich, L Schneider, L Schäpermeier, B Bischl, P Kerschke, ...
Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic …, 2023
1 2023 Improving Automated Algorithm Selection by Advancing Fitness Landscape Analysis RP Prager
arXiv preprint arXiv:2312.03105, 2023
2023 A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes M Vinzent Seiler, RP Prager, P Kerschke, H Trautmann
arXiv e-prints, arXiv: 2204.05752, 2022
2022 