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Benchmarking heuristic algorithms is vital to understand under which conditions and on what kind of problems certain algorithms perform well.
Boah: A tool suite for multi-fidelity bayesian optimization & analysis of hyperparameters
Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Marben, J., Müller, P., and Hutter, F · 1908
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Using genetic algorithms to solve np-complete problems
Jong, K. A. D., and Spears, W. M · 1989
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Sensitivity estimates for nonlinear mathematical models
Sobol’, I · 1993
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No free lunch theorems for optimization
Wolpert, D., and Macready, W · 1997
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A fast and elitist multiobjective genetic algorithm: Nsga-ii
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T · 2002
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A restart cma evolution strategy with increasing population size
Auger, A., and Hansen, N · 2005
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A note on research methodology and benchmarking optimization algorithms
Brownlee, J · 2007
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Benchmarking a bi-population cma-es on the bbob-2009 function testbed
Hansen, N · 2009
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Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions
Hansen, N., Finck, S., Ros, R., and Auger, A · 2009
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Automatic configuration of multi-objective aco algorithms
López-Ibáñez, M., and Stützle, T · 2010
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Impacts of invariance in search: When cma-es and pso face ill-conditioned and non-separable problems
Hansen, N., Ros, R., Mauny, N., Schoenauer, M., and Auger, A · 2011
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Exploratory landscape analysis
Mersmann, O., Bischl, B., Trautmann, H., Preuss, M., Weihs, C., and Rudolph, G · 2011
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The automatic design of multiobjective ant colony optimization algorithms
López-Ibánez, M., and Stutzle, T · 2012
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An efficient approach for assessing hyperparameter importance
Hutter, F., Hoos, H., and Leyton-Brown, K · 2014
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Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution
Glotić, A., and Zamuda, A · 2015
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Structural bias in population-based algorithms
Kononova, A. V., Corne, D. W., Wilde, P. D., Shneer, V., and Caraffini, F · 2015
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Algorithm selection for black-box continuous optimization problems: A survey on methods and challenges
Muñoz, M. A., Sun, Y., Kirley, M., and Halgamuge, S. K · 2015
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Water evaporation optimization: A novel physically inspired optimization algorithm
Kaveh, A., and Bakhshpoori, T · 2016
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" why should i trust you?" explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
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Simplify your covariance matrix adaptation evolution strategy
Beyer, H.-G., and Sendhoff, B · 2017
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A unified approach to interpreting model predictions
Lundberg, S. M., and Lee, S.-I · 2017
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Problem definitions and evaluation criteria for the cec 2017 competition on constrained real-parameter optimization
Wu, G., Mallipeddi, R., and Suganthan, P. N · 2017
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Proposal of benchmark problem based on real-world car structure design optimization
Kohira, T., Kemmotsu, H., Akira, O., and Tatsukawa, T · 2018
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Coyote optimization algorithm: A new metaheuristic for global optimization problems
Pierezan, J., and Dos Santos Coelho, L · 2018
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Nevergrad - A gradient-free optimization platform
Rapin, J., and Teytaud, O · 2018
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A study on rotation invariance in differential evolution
Caraffini, F., and Neri, F · 2019
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What’s inside the black-box? a genetic programming method for interpreting complex machine learning models
Evans, B. P., Xue, B., and Zhang, M · 2019
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Automated algorithm selection on continuous black-box problems by combining exploratory landscape analysis and machine learning
Kerschke, P., and Trautmann, H · 2019
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Success history applied to expert system for underwater glider path planning using differential evolution
Zamuda, A., and Sosa, J. D. H · 2019
Cited alongside, same era.
Benchmarking in optimization: Best practice and open issues, 2020
Bartz-Beielstein, T., Doerr, C., van den Berg, D., Bossek, J., Chandrasekaran, S., Eftimov, T., Fischbach, A., Kerschke, P., Cava, W. L., Lopez-Ibanez, M., Malan, K. M., Moore, J. H., Naujoks, B., Orzechowski, P., Volz, V., Wagner, M., and Weise, T · 2020
Cited alongside, same era.
Learning the characteristics of engineering optimization problems with applications in automotive crash
Long, F. X., van Stein, B., Frenzel, M., Krause, P., Gitterle, M., and Bäck, T · 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
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A comparison of global sensitivity analysis methods for explainable ai with an application in genomic prediction
Stein, B. V., Raponi, E., Sadeghi, Z., Bouman, N., Van Ham, R. C. H. J., and Bäck, T · 2022
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Explainable landscape analysis in automated algorithm performance prediction, 2022
Trajanov, R., Dimeski, S., Popovski, M., Korošec, P., and Eftimov, T · 2022
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Improving nevergrad’s algorithm selection wizard ngopt through automated algorithm configuration
Trajanov, R., Nikolikj, A., Cenikj, G., Teytaud, F., Videau, M., Teytaud, O., Eftimov, T., López-Ibáñez, M., and Doerr, C · 2022
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Recent advances in selection hyper-heuristics
Drake, J. H., Kheiri, A., Özcan, E., and Burke, E. K · 2020
Cited alongside, same era.
Applying genetic programming to improve interpretability in machine learning models
Ferreira, L. A., Guimarães, F. G., and Silva, R · 2020
Cited alongside, same era.
Genetic programming for evolving a front of interpretable models for data visualization
Lensen, A., Xue, B., and Zhang, M · 2020
Cited alongside, same era.
From local explanations to global understanding with explainable ai for trees
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I · 2020
Cited alongside, same era.
Per-instance configuration of the modularized cma-es by means of classifier chains and exploratory landscape analysis
Prager, R. P., Trautmann, H., Wang, H., Bäck, T. H., and Kerschke, P · 2020
Cited alongside, same era.
An easy-to-use real-world multi-objective optimization problem suite
Tanabe, R., and Ishibuchi, H · 2020
Cited alongside, same era.
Quantifying the impact of boundary constraint handling methods on differential evolution
Boks, R., Kononova, A. V., and Wang, H · 2021
Cited alongside, same era.
Improving nevergrad’s algorithm selection wizard ngopt through automated algorithm configuration
Trajanov, R., Nikolikj, A., Cenikj, G., Teytaud, F., Videau, M., Teytaud, O., Eftimov, T., López-Ibáñez, M., and Doerr, C · 2022
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Gsareport: Easy to use global sensitivity reporting
Van Stein, B., and Raponi, E · 2022
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Bias: A toolbox for benchmarking structural bias in the continuous domain
Vermetten, D., van Stein, B., Caraffini, F., Minku, L. L., and Kononova, A. V · 2022
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Iohanalyzer: Detailed performance analyses for iterative optimization heuristics
Wang, H., Vermetten, D., Ye, F., Doerr, C., and Bäck, T · 2022
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Evolutionary algorithms for parameter optimization—thirty years later
Bäck, T. H. W., Kononova, A. V., van Stein, B., Wang, H., Antonov, K. A., Kalkreuth, R. T., de Nobel, J., Vermetten, D., de Winter, R., and Ye, F · 2023
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Comparison of three versions of whale optimization algorithm (woa) on the bbob test suite
Espinoza, O., Rodríguez-Vázquez, K., Hernández, C. I., and Rodriguez-Romo, S · 2023
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Covariance matrix adaptation map-annealing
Fontaine, M., and Nikolaidis, S · 2023
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The importance of being constrained: dealing with infeasible solutions in differential evolution and beyond
Kononova, A. V., Vermetten, D., Caraffini, F., Mitran, M.-A., and Zaharie, D · 2023
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Using knowledge graphs for performance prediction of modular optimization algorithms
Kostovska, A., Vermetten, D., Dzeroski, S., Panov, P., Eftimov, T., and Doerr, C · 2023
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Bbob instance analysis: Landscape properties and algorithm performance across problem instances
Long, F. X., Vermetten, D., van Stein, B., and Kononova, A. V · 2023
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Patterns of convergence and bound constraint violation in differential evolution on sbox-cost benchmarking suite
Mitran, M.-A., Kononova, A. V., Caraffini, F., and Zaharie, D · 2023
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Optimizing cma-es with cma-es
Thomaser., A., Vogt., M., Bäck., T., and Kononova, A · 2023
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Identifying Properties of Real-World Optimisation Problems Through a Questionnaire
van der Blom, K., Deist, T. M., Volz, V., Marchi, M., Nojima, Y., Naujoks, B., Oyama, A., and Tušar, T · 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
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Deep bias: Detecting structural bias using explainable ai
Van Stein, B., Vermetten, D., Caraffini, F., and Kononova, A. V · 2023
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Modular differential evolution
Vermetten, D., Caraffini, F., Kononova, A. V., and Bäck, T · 2023
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nikivanstein/iohxplainer: v0.9.1 pre-final release
Anonymous · 2024
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Large-scale benchmarking of metaphor-based optimization heuristics, 2024
Vermetten, D., Doerr, C., Wang, H., Kononova, A. V., and Bäck, T · 2024
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Evolutionary Approaches to Explainable Machine Learning
Zhou, R., and Hu, T · 2024
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