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Finding the best configuration of algorithms' hyperparameters for a given optimization problem is an important task in evolutionary computation.
An analysis of the behavior of a class of genetic adaptive systems
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Hyun-Sook Yoon and Byung-Ro Moon · 2002
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M. Birattari, Z. Yuan, P. Balaprakash, and T. Stützle · 2010
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R. Li, M. T. Emmerich, J. Eggermont, T. Bäck, M. Schütz, J. Dijkstra, and J. H. Reiber · 2013
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Automatically improving the anytime behaviour of optimisation algorithms
M. López-Ibánez and T. Stützle · 2014
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Partition crossover for pseudo-boolean optimization
R. Tinós, L. D. Whitley, and F. Chicano · 2015
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The irace package: Iterated racing for automatic algorithm configuration
M. López-Ibáñez, J. Dubois-Lacoste, L. P. Cáceres, M. Birattari, and T. Stützle · 2016
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Taking the Human Out of the Loop: A review of Bayesian optimization
B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. de Freitas · 2016
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Fast genetic algorithms
B. Doerr, H. P. Le, R. Makhmara, and T. D. Nguyen · 2017
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An experimental study of adaptive capping in irace
L. Pérez Cáceres, M. López-Ibáñez, H. Hoos, and T. Stützle · 2017
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Automatic configuration of deep neural networks with parallel efficient global optimization
B. van Stein, H. Wang, and T. Bäck · 2019
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Interpolating local and global search by controlling the variance of standard bit mutation
F. Ye, C. Doerr, and T. Bäck · 2019
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Anytime behavior of inexact TSP solvers and perspectives for automated algorithm selection
J. Bossek, P. Kerschke, and H. Trautmann · 2020
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A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms
J. Bossek, P. Kerschke, and H. Trautmann · 2020
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Benchmarking discrete optimization heuristics with IOHprofiler
C. Doerr, F. Ye, N. Horesh, H. Wang, O. M. Shir, and T. Bäck · 2020
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How crossover speeds up building block assembly in genetic algorithms
D. Sudholt · 2017
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A new acquisition function for Bayesian optimization based on the moment-generating function
H. Wang, B. van Stein, M. Emmerich, and T. Bäck · 2017
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A simple proof for the usefulness of crossover in black-box optimization
E. Carvalho Pinto and C. Doerr · 2018
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Standard steady state genetic algorithms can hillclimb faster than mutation-only evolutionary algorithms
D. Corus and P. S. Oliveto · 2018
Cited alongside, same era.
IOHprofiler: A benchmarking and profiling tool for iterative optimization heuristics
C. Doerr, H. Wang, F. Ye, S. van Rijn, and T. Bäck · 2018
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A tutorial on Bayesian optimization
P. I. Frazier · 2018
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Analysis of the performance of algorithm configurators for search heuristics with global mutation operators
G. T. Hall, P. S. Oliveto, and D. Sudholt · 2020
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Algorithm selection of anytime algorithms
A. D. Jesus, A. Liefooghe, B. Derbel, and L. Paquete · 2020
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The benefits of population diversity in evolutionary algorithms: A survey of rigorous runtime analyses
D. Sudholt · 2020
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Analyzing adaptive parameter landscapes in parameter adaptation methods for differential evolution
R. Tanabe · 2020
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Selecting a diverse set of benchmark instances from a tunable model problem for black-box discrete optimization algorithms
T. Weise, Y. Chen, X. Li, and Z. Wu · 2020
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Benchmarking a ( μ + λ ) (\mu+\lambda) genetic algorithm with configurable crossover probability
F. Ye, H. Wang, C. Doerr, and T. Bäck · 2020
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Towards large scale automated algorithm design by integrating modular benchmarking frameworks
A. Aziz-Alaoui, C. Doerr, and J. Dréo · 2021
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Acviz: A tool for the visual analysis of the configuration of algorithms with irace
M. de Souza, M. Ritt, M. López-Ibáñez, and L. P. Cáceres · 2021
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A model of anytime algorithm performance for bi-objective optimization
A. D. Jesus, L. Paquete, and A. Liefooghe · 2021
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Partition crossover for continuous optimization: ePX
R. Tinós, D. Whitley, F. Chicano, and G. Ochoa · 2021
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Data sets for the study ”Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance”
F. Ye, C. Doerr, H. Wang, and T. Bäck · 2021
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On the impact of the performance metric on efficient algorithm configuration
G. T. Hall, P. S. Oliveto, and D. Sudholt · 2022
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IOHanalyzer: Performance analysis for iterative optimization heuristic
H. Wang, D. Vermetten, F. Ye, C. Doerr, and T. Bäck · 2022
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