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In many recent works, the potential of Exploratory Landscape Analysis (ELA) features to numerically characterize, in particular, single-objective continuous optimization problems has been demonstrated.
Transformers without tears: Improving the normalization of self-attention
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Nearest Neighbor Pattern Classification
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On the distribution of points in a cube and the approximate evaluation of integrals
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The Algorithm Selection Problem
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A comparison of three methods for selecting values of input variables in the analysis of output from a computer code
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Support-Vector Networks
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Fitness distance correlation as a measure of problem difficulty for genetic algorithms
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Convolutional Networks for Images, Speech, and Time Series
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Long short-term memory
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Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
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Random Forests
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A fast and elitist multiobjective genetic algorithm: Nsga-ii
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Performance evaluation of an advanced local search evolutionary algorithm
Auger, A. and Hansen, N. (2005) · 2005
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Scalable Test Problems for Evolutionary Multiobjective Optimization
Deb, K., Thiele, L., Laumanns, M., and Zitzler, E. (2005) · 2005
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Omni-optimizer: A procedure for single and multi-objective optimization
Deb, K. and Tiwari, S. (2005) · 2005
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The dispersion metric and the cma evolution strategy
Lunacek, M. and Whitley, D. (2006) · 2006
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Sms-emoa: Multiobjective selection based on dominated hypervolume
Beume, N., Naujoks, B., and Emmerich, M. (2007) · 2007
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Moea/d: A multiobjective evolutionary algorithm based on decomposition
Zhang, Q. and Li, H. (2007) · 2007
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Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions
Hansen, N., Finck, S., Ros, R., and Auger, A. (2009) · 2009
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Paramils: an automatic algorithm configuration framework
Hutter, F., Hoos, H. H., Leyton-Brown, K., and Stützle, T. (2009) · 2009
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Benchmarking evolutionary algorithms: Towards exploratory landscape analysis
Mersmann, O., Preuss, M., and Trautmann, H. (2010) · 2010
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Exploratory landscape analysis
Mersmann, O., Bischl, B., Trautmann, H., Preuss, M., Weihs, C., and Rudolph, G. (2011) · 2011
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Transformer feed-forward layers are key-value memories
Geva, M., Schuster, R., Berant, J., and Levy, O. (2020) · 2012
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Exploratory landscape analysis of continuous space optimization problems using information content
Muñoz, M. A., Kirley, M., and Halgamuge, S. K. (2014) · 2014
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Towards objective measures of algorithm performance across instance space
Smith-Miles, K., Baatar, D., Wreford, B., and Lewis, R. (2014) · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C. (2015) · 2015
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Detecting funnel structures by means of exploratory landscape analysis
Kerschke, P., Preuss, M., Wessing, S., and Trautmann, H. (2015) · 2015
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Deep learning as a competitive feature-free approach for automated algorithm selection on the traveling salesperson problem
Seiler, M. V., Pohl, J., Bossek, J., Kerschke, P., and Trautmann, H. (2020) · 2020
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A recommender system for metaheuristic algorithms for continuous optimization based on deep recurrent neural networks
Tian, Y., Peng, S., Zhang, X., Rodemann, T., Tan, K. C., and Jin, Y. (2020) · 2020
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An empirical study of training self-supervised vision transformers
Chen, X., Xie, S., and He, K. (2021) · 2021
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PCT: Point Cloud Transformer
Guo, M.-H., Cai, J.-X., Liu, Z.-N., Mu, T.-J., Martin, R. R., and Hu, S.-M. (2021) · 2021
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Towards feature-free automated algorithm selection for single-objective continuous black-box optimization
Prager, R. P., Seiler, M. V., Trautmann, H., and Kerschke, P. (2021) · 2021
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Ba, J. L., Kiros, J. R., and Hinton, G. E. (2016) · 2016
Cited alongside, same era.
The R-Package FLACCO for Exploratory Landscape Analysis with Applications to Multi-Objective Optimization Problems
Kerschke, P. and Trautmann, H. (2016) · 2016
Cited alongside, same era.
The irace package: Iterated racing for automatic algorithm configuration
López-Ibáñez, M., Dubois-Lacoste, J., Cáceres, L. P., Birattari, M., and Stützle, T. (2016) · 2016
Cited alongside, same era.
Language modeling with gated convolutional networks
Dauphin, Y. N., Fan, A., Auli, M., and Grangier, D. (2017) · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017) · 2017
Cited alongside, same era.
Hypervolume indicator gradient ascent multi-objective optimization
Wang, H., Deutz, A., Bäck, T., and Emmerich, M. (2017) · 2017
Cited alongside, same era.
Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O. (2018) · 2018
Cited alongside, same era.
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Using Well-Understood Single-Objective Functions in Multiobjective Black-Box Optimization Test Suites
Brockhoff, D., Auger, A., Hansen, N., and Tušar, T. (2022) · 2022
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Smac3: A versatile bayesian optimization package for hyperparameter optimization
Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Deng, D., Benjamins, C., Ruhkopf, T., Sass, R., and Hutter, F. (2022) · 2022
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Automated algorithm selection in single-objective continuous optimization: A comparative study of deep learning and landscape analysis methods
Prager, R. P., Seiler, M. V., Trautmann, H., and Kerschke, P. (2022) · 2022
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On the potential of automated algorithm configuration on multi-modal multi-objective optimization problems
Rook, J., Trautmann, H., Bossek, J., and Grimme, C. (2022) · 2022
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A survey of methods for automated algorithm configuration
Schede, E., Brandt, J., Tornede, A., Wever, M., Bengs, V., Hüllermeier, E., and Tierney, K. (2022) · 2022
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An R Package Implementing the Multi-Objective Landscape Explorer (MOLE)
Schäpermeier, L. (2022) · 2022
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A collection of deep learning-based feature-free approaches for characterizing single-objective continuous fitness landscapes
Seiler, M. V., Prager, R. P., Kerschke, P., and Trautmann, H. (2022) · 2022
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TransOpt: Transformer-based Representation Learning for Optimization Problem Classification
Cenikj, G., Petelin, G., and Eftimov, T. (2023) · 2023
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Exploratory landscape analysis
Kerschke, P. and Preuss, M. (2023) · 2023
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Neural networks as black-box benchmark functions optimized for exploratory landscape features
Prager, R. P., Dietrich, K., Schneider, L., Schäpermeier, L., Bischl, B., Kerschke, P., Trautmann, H., and Mersmann, O. (2023) · 2023
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Nullifying the inherent bias of non-invariant exploratory landscape analysis features
Prager, R. P. and Trautmann, H. (2023) · 2023
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Instance space analysis for algorithm testing: Methodology and software tools
Smith-Miles, K. and Muñoz, M. A. (2023) · 2023
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Doe2vec: Deep-learning based features for exploratory landscape analysis
van Stein, B., Long, F. X., Frenzel, M., Krause, P., Gitterle, M., and Bäck, T. (2023) · 2023
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Tools for landscape analysis of optimisation problems in procedural content generation for games
Volz, V., Naujoks, B., Kerschke, P., and Tušar, T. (2023) · 2023
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Synergies of Deep and Classical Exploratory Landscape Features for Automated Algorithm Selection
Seiler, M., Škvorc, U., Doerr, C., and Trautmann, H. (2024) · 2024
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Expressiveness and Robustness of Landscape Features
Renau, Q., Dréo, J., Doerr, C., and Doerr, B. (2019) · 2051
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