Carola Doerr

Duri Andrea Janett

Algorithmica, October, 2024

Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB.

[BibT_eX]

[DOI]

ACM Trans. Evol. Learn. Optim., September, 2024

MA-BBOB - Reproducibility and Additional Data.

[BibT_eX]

[DOI]

Dataset, May, 2024

Benchmarking Dynamic Binary Value Problems with IOHprofiler - Reproducibility files.

[BibT_eX]

[DOI]

Dataset, April, 2024

Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization - Reproducibility Files.

[BibT_eX]

[DOI]

Dataset, April, 2024

Large-Scale Benchmarking of Metaphor-Based Optimization Heuristics - Reproducibility Files.

[BibT_eX]

[DOI]

Dataset, January, 2024

Heuristic approaches to obtain low-discrepancy point sets via subset selection.

[BibT_eX]

[DOI]

François Clément

Luís Paquete

J. Complex., 2024

IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics.

[BibT_eX]

[DOI]

Evol. Comput., 2024

Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization.

[BibT_eX]

[DOI]

CoRR, 2024

Transforming the Challenge of Constructing Low-Discrepancy Point Sets into a Permutation Selection Problem.

[BibT_eX]

[DOI]

CoRR, 2024

A Survey of Meta-features Used for Automated Selection of Algorithms for Black-box Single-objective Continuous Optimization.

[BibT_eX]

[DOI]

CoRR, 2024

Using the Empirical Attainment Function for Analyzing Single-objective Black-box Optimization Algorithms.

[BibT_eX]

[DOI]

CoRR, 2024

Empirical Analysis of the Dynamic Binary Value Problem with IOHprofiler.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XVIII, 2024

Learned Features vs. Classical ELA on Affine BBOB Functions.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XVIII, 2024

Hybridizing Target- and SHAP-Encoded Features for Algorithm Selection in Mixed-Variable Black-Box Optimization.

[BibT_eX]

[DOI]

Konstantin Dietrich

Raphael Patrick Prager

Heike Trautmann

Proceedings of the Parallel Problem Solving from Nature - PPSN XVIII, 2024

Large-Scale Benchmarking of Metaphor-Based Optimization Heuristics.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2024

Benchmarking and Analyzing Iterative Optimization Heuristics with IOHprofiler.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2024

Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2024

Quantifying Individual and Joint Module Impact in Modular Optimization Frameworks.

[BibT_eX]

[DOI]

Proceedings of the IEEE Congress on Evolutionary Computation, 2024

Generalization Ability of Feature-Based Performance Prediction Models: A Statistical Analysis Across Benchmarks.

[BibT_eX]

[DOI]

Proceedings of the IEEE Congress on Evolutionary Computation, 2024

2023

OPTION: OPTImization Algorithm Benchmarking ONtology.

[BibT_eX]

[DOI]

IEEE Trans. Evol. Comput., December, 2023

MA-BBOB - Reproducibility and Additional Data.

[BibT_eX]

[DOI]

Dataset, December, 2023

Run Time Analysis for Random Local Search on Generalized Majority Functions.

[BibT_eX]

[DOI]

Martin S. Krejca

IEEE Trans. Evol. Comput., October, 2023

Using Affine Combinations of BBOB Problems for Performance Assessment - Code and Data.

[BibT_eX]

[DOI]

Diederick Vermetten

Nathanaël Carraz Rakotonirina

Dataset, February, 2023

Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms - Code and Data.

[BibT_eX]

[DOI]

Dataset, February, 2023

MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts.

[BibT_eX]

[DOI]

CoRR, 2023

Constructing Optimal L∞ Star Discrepancy Sets.

[BibT_eX]

[DOI]

CoRR, 2023

Optimizing with Low Budgets: a Comparison on the Black-box Optimization Benchmarking Suite and OpenAI Gym.

[BibT_eX]

[DOI]

Elena Raponi

Jérémy Rapin

Olivier Teytaud

CoRR, 2023

Benchmarking Iterative Optimization Heuristics with IOHprofiler (invited paper).

[BibT_eX]

[DOI]

Proceedings of the 23rd Conference Information Technologies, 2023

Using Affine Combinations of BBOB Problems for Performance Assessment.

[BibT_eX]

[DOI]

Diederick Vermetten

Andries Petrus Engelbrecht

Proceedings of the Genetic and Evolutionary Computation Conference, 2023

Comparison of Bayesian Optimization Algorithms for BBOB Problems in Dimensions 10 and 60.

[BibT_eX]

[DOI]

Proceedings of the Companion Proceedings of the Conference on Genetic and Evolutionary Computation, 2023

Algorithm Instance Footprint: Separating Easily Solvable and Challenging Problem Instances.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2023

Assessing the Generalizability of a Performance Predictive Model.

[BibT_eX]

[DOI]

Peter Korosec

Proceedings of the Companion Proceedings of the Conference on Genetic and Evolutionary Computation, 2023

Comparing Algorithm Selection Approaches on Black-Box Optimization Problems.

[BibT_eX]

[DOI]

Proceedings of the Companion Proceedings of the Conference on Genetic and Evolutionary Computation, 2023

Benchmarking and analyzing iterative optimization heuristics with IOHprofiler.

[BibT_eX]

[DOI]

Proceedings of the Companion Proceedings of the Conference on Genetic and Evolutionary Computation, 2023

Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2023

DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2023

Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization.

[BibT_eX]

[DOI]

Proceedings of the Companion Proceedings of the Conference on Genetic and Evolutionary Computation, 2023

Bridging Theory and Practice in Evolutionary Computation?

[BibT_eX]

[DOI]

Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2023

Using Automated Algorithm Configuration for Parameter Control.

[BibT_eX]

[DOI]

Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2023

RF+clust for Leave-One-Problem-Out Performance Prediction.

[BibT_eX]

[DOI]

Ana Nikolikj

Proceedings of the Applications of Evolutionary Computation - 26th European Conference, 2023

Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms.

[BibT_eX]

[DOI]

Proceedings of the Applications of Evolutionary Computation - 26th European Conference, 2023

Sensitivity Analysis of RF+clust for Leave-One-Problem-Out Performance Prediction.

[BibT_eX]

[DOI]

Proceedings of the IEEE Congress on Evolutionary Computation, 2023

MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Automated Machine Learning, 2023

PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Automated Machine Learning, 2023

Self-Adjusting Weighted Expected Improvement for Bayesian Optimization.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Automated Machine Learning, 2023

2022

Data Sets for the study "Non-Elitist Selection Can Improve the Performance of Irace".

[BibT_eX]

[DOI]

Dataset, April, 2022

Per-Run Algorithm Selection with Warm-starting using Trajectory-based Features - Data.

[BibT_eX]

[DOI]

Dataset, April, 2022

Linking Problem Landscape Features with the Performance of Individual CMA-ES Modules - Data.

[BibT_eX]

[DOI]

Dataset, February, 2022

Analyzing the Impact of Undersampling on the Benchmarkingand Configuration of Evolutionary Algorithms - Dataset.

[BibT_eX]

[DOI]

Dataset, January, 2022

IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics.

[BibT_eX]

[DOI]

ACM Trans. Evol. Learn. Optim., 2022

Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance.

[BibT_eX]

[DOI]

IEEE Trans. Evol. Comput., 2022

Black-Box Optimization Revisited: Improving Algorithm Selection Wizards Through Massive Benchmarking.

[BibT_eX]

[DOI]

Laurent Meunier

Herilalaina Rakotoarison

IEEE Trans. Evol. Comput., 2022

Guest Editorial Special Issue on Benchmarking Sampling-Based Optimization Heuristics: Methodology and Software.

[BibT_eX]

[DOI]

IEEE Trans. Evol. Comput., 2022

Star discrepancy subset selection: Problem formulation and efficient approaches for low dimensions.

[BibT_eX]

[DOI]

François Clément

Luís Paquete

J. Complex., 2022

Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis.

[BibT_eX]

[DOI]

CoRR, 2022

PI is back! Switching Acquisition Functions in Bayesian Optimization.

[BibT_eX]

[DOI]

CoRR, 2022

Chaining of Numerical Black-box Algorithms: Warm-Starting and Switching Points.

[BibT_eX]

[DOI]

CoRR, 2022

Non-Elitist Selection among Survivor Configurations can Improve the Performance of Irace.

[BibT_eX]

[DOI]

Diederick L. Vermetten

Andries Petrus Engelbrecht

CoRR, 2022

Fixed-Target Runtime Analysis.

[BibT_eX]

[DOI]

Algorithmica, 2022

Explainable Model-specific Algorithm Selection for Multi-Label Classification.

[BibT_eX]

[DOI]

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2022

Non-elitist Selection Can Improve the Performance of Irace.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XVII, 2022

Improving Nevergrad's Algorithm Selection Wizard NGOpt Through Automated Algorithm Configuration.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XVII, 2022

Per-run Algorithm Selection with Warm-Starting Using Trajectory-Based Features.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XVII, 2022

High Dimensional Bayesian Optimization with Kernel Principal Component Analysis.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XVII, 2022

Automated configuration of genetic algorithms by tuning for anytime performance: hot-off-the-press track at GECCCO 2022.

[BibT_eX]

[DOI]

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022

IOHanalyzer: Detailed performance analyses for iterative optimization heuristics: hot-off-the-press track @ GECCO 2022.

[BibT_eX]

[DOI]

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022

Analyzing the impact of undersampling on the benchmarking and configuration of evolutionary algorithms.

[BibT_eX]

[DOI]

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Boston, Massachusetts, USA, July 9, 2022

Automated algorithm selection for radar network configuration.

[BibT_eX]

[DOI]

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Boston, Massachusetts, USA, July 9, 2022

The importance of landscape features for performance prediction of modular CMA-ES variants.

[BibT_eX]

[DOI]

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Boston, Massachusetts, USA, July 9, 2022

Benchmarking and analyzing iterative optimization heuristics with IOH profiler.

[BibT_eX]

[DOI]

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022

SELECTOR: selecting a representative benchmark suite for reproducible statistical comparison.

[BibT_eX]

[DOI]

Gjorgjina Cenikj

Ryan Dieter Lang

Peter Korosec

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Boston, Massachusetts, USA, July 9, 2022

Theory-inspired parameter control benchmarks for dynamic algorithm configuration.

[BibT_eX]

[DOI]

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Boston, Massachusetts, USA, July 9, 2022

Trajectory-based Algorithm Selection with Warm-starting.

[BibT_eX]

[DOI]

Proceedings of the IEEE Congress on Evolutionary Computation, 2022

Fast Re-Optimization of LeadingOnes with Frequent Changes.

[BibT_eX]

[DOI]

Nina Bulanova

Arina Buzdalova

Proceedings of the IEEE Congress on Evolutionary Computation, 2022

2021

IOHanalyzer version 0.1.6.1 + example datasets.

[BibT_eX]

[DOI]

Dataset, October, 2021

Data sets for the study "Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance.".

[BibT_eX]

[DOI]

Dataset, May, 2021

Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking Frameworks.

[BibT_eX]

[DOI]

Amine Aziz-Alaoui

Dataset, April, 2021

Data Sets for the study "Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection".

[BibT_eX]

[DOI]

Dataset, February, 2021

Exploratory Landscape Analysis Feature Values for the 24 Noiseless BBOB Functions.

[BibT_eX]

[DOI]

Dataset, January, 2021

Maximizing Drift Is Not Optimal for Solving OneMax.

[BibT_eX]

[DOI]

Nathan Buskulic

Evol. Comput., 2021

Optimal Static Mutation Strength Distributions for the (1+λ) Evolutionary Algorithm on OneMax.

[BibT_eX]

[DOI]

Maxim Buzdalov

CoRR, 2021

Star Discrepancy Subset Selection: Problem Formulation and Efficient Approaches for Low Dimensions.

[BibT_eX]

[DOI]

Luís Paquete

CoRR, 2021

Self-Adjusting Mutation Rates with Provably Optimal Success Rules.

[BibT_eX]

[DOI]

Algorithmica, 2021

Leveraging benchmarking data for informed one-shot dynamic algorithm selection.

[BibT_eX]

[DOI]

Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

Tuning as a means of assessing the benefits of new ideas in interplay with existing algorithmic modules.

[BibT_eX]

[DOI]

Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

The impact of hyper-parameter tuning for landscape-aware performance regression and algorithm selection.

[BibT_eX]

[DOI]

Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

Personalizing performance regression models to black-box optimization problems.

[BibT_eX]

[DOI]

Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

Optimal static mutation strength distributions for the (1 + λ) evolutionary algorithm on OneMax.

[BibT_eX]

[DOI]

Maxim Buzdalov

Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

Towards large scale automated algorithm design by integrating modular benchmarking frameworks.

[BibT_eX]

[DOI]

Amine Aziz-Alaoui

Marcella Scoczynski Ribeiro Martins

Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

MATE: A Model-Based Algorithm Tuning Engine - A Proof of Concept Towards Transparent Feature-Dependent Parameter Tuning Using Symbolic Regression.

[BibT_eX]

[DOI]

Mohamed El Yafrani

Proceedings of the Evolutionary Computation in Combinatorial Optimization, 2021

Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions.

[BibT_eX]

[DOI]

Proceedings of the Applications of Evolutionary Computation, 2021

Towards Feature-Based Performance Regression Using Trajectory Data.

[BibT_eX]

[DOI]

Anja Jankovic

Proceedings of the Applications of Evolutionary Computation, 2021

Blending Dynamic Programming with Monte Carlo Simulation for Bounding the Running Time of Evolutionary Algorithms.

[BibT_eX]

[DOI]

Proceedings of the IEEE Congress on Evolutionary Computation, 2021

2020

Experimental Data Set for the study "Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy".

[BibT_eX]

[DOI]

Dataset, June, 2020

Experimental Data Sets for the study "Benchmarking a (μ+λ) Genetic Algorithm with Configurable Crossover Probability".

[BibT_eX]

[DOI]

Dataset, April, 2020

Experimental Data Sets for the study "Benchmarking a (μ+λ) Genetic Algorithm with Configurable Crossover Probability".

[BibT_eX]

[DOI]

Dataset, April, 2020

The Experimental Data Sets for the study "Benchmarking a (μ+λ) Genetic Algorithm withConfigurable Crossover Probability".

[BibT_eX]

[DOI]

Dataset, April, 2020

Theory of Parameter Control for Discrete Black-Box Optimization: Provable Performance Gains Through Dynamic Parameter Choices.

[BibT_eX]

[DOI]

Proceedings of the Theory of Evolutionary Computation, 2020

Complexity Theory for Discrete Black-Box Optimization Heuristics.

[BibT_eX]

[DOI]

Proceedings of the Theory of Evolutionary Computation, 2020

Optimal parameter choices via precise black-box analysis.

[BibT_eX]

[DOI]

Jing Yang

Theor. Comput. Sci., 2020

Squirrel: A Switching Hyperparameter Optimizer.

[BibT_eX]

[DOI]

Katharina Eggensperger

CoRR, 2020

IOHanalyzer: Performance Analysis for Iterative Optimization Heuristic.

[BibT_eX]

[DOI]

CoRR, 2020

Benchmarking in Optimization: Best Practice and Open Issues.

[BibT_eX]

[DOI]

Thomas Bartz-Beielstein

Marcella Scoczynski Ribeiro Martins

Jakob Bossek

Sowmya Chandrasekaran

CoRR, 2020

MATE: A Model-based Algorithm Tuning Engine.

[BibT_eX]

[DOI]

Mohamed El Yafrani

CoRR, 2020

Initial Design Strategies and their Effects on Sequential Model-Based Optimization.

[BibT_eX]

[DOI]

Jakob Bossek

Pascal Kerschke

CoRR, 2020

Benchmarking discrete optimization heuristics with IOHprofiler.

[BibT_eX]

[DOI]

Appl. Soft Comput., 2020

Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapes.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence, 2020

Benchmarking a (μ +λ ) Genetic Algorithm with Configurable Crossover Probability.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

High Dimensional Bayesian Optimization Assisted by Principal Component Analysis.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

Variance Reduction for Better Sampling in Continuous Domains.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm.

[BibT_eX]

[DOI]

Arina Buzdalova

Anna Rodionova

Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

Optimal Mutation Rates for the (1+λ ) EA on OneMax.

[BibT_eX]

[DOI]

Maxim Buzdalov

Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

Evolving Sampling Strategies for One-Shot Optimization Tasks.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

Benchmarking and analyzing iterative optimization heuristics with IOHprofiler.

[BibT_eX]

[DOI]

Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

Integrated vs. sequential approaches for selecting and tuning CMA-ES variants.

[BibT_eX]

[DOI]

Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

Towards dynamic algorithm selection for numerical black-box optimization: investigating BBOB as a use case.

[BibT_eX]

[DOI]

Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

Dynamic control parameter choices in evolutionary computation: GECCO 2020 tutorial.

[BibT_eX]

[DOI]

Gregor Papa

Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

Landscape-aware fixed-budget performance regression and algorithm selection for modular CMA-ES variants.

[BibT_eX]

[DOI]

Anja Jankovic

Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

Initial design strategies and their effects on sequential model-based optimization: an exploratory case study based on BBOB.

[BibT_eX]

[DOI]

Jakob Bossek

Pascal Kerschke

Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

Optimization of Chance-Constrained Submodular Functions.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Theory of Iterative Optimization Heuristics: From Black-Box Complexity over Algorithm Design to Parameter Control.

[BibT_eX]

[DOI]

, 2020

2019

The query complexity of a permutation-based variant of Mastermind.

[BibT_eX]

[DOI]

Discret. Appl. Math., 2019

Theory of Randomized Optimization Heuristics (Dagstuhl Reports 19431).

[BibT_eX]

[DOI]

Dagstuhl Reports, 2019

One-Shot Decision-Making with and without Surrogates.

[BibT_eX]

[DOI]

CoRR, 2019

Sequential vs. Integrated Algorithm Selection and Configuration: A Case Study for the Modular CMA-ES.

[BibT_eX]

[DOI]

CoRR, 2019

Hyper-Parameter Tuning for the (1+(λ, λ)) GA.

[BibT_eX]

[DOI]

Nguyen Dang

CoRR, 2019

Preface to the Special Issue on Theory of Genetic and Evolutionary Computation.

[BibT_eX]

[DOI]

Dirk Sudholt

Algorithmica, 2019

Solving Problems with Unknown Solution Length at Almost No Extra Cost.

[BibT_eX]

[DOI]

Algorithmica, 2019

Fixed-target runtime analysis of the (1 + 1) EA with resampling.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

Online selection of CMA-ES variants.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2019

Offspring population size matters when comparing evolutionary algorithms with self-adjusting mutation rates.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2019

Expressiveness and robustness of landscape features.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

Adaptive landscape analysis.

[BibT_eX]

[DOI]

Anja Jankovic

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

Illustrating the trade-off between time, quality, and success probability in heuristic search: a discussion paper.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

Coupling the design of benchmark with algorithm in landscape-aware solver design.

[BibT_eX]

[DOI]

Yann Semet

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

Making a case for (Hyper-)parameter tuning as benchmark problems.

[BibT_eX]

[DOI]

Pascal Kerschke

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

Fast re-optimization via structural diversity.

[BibT_eX]

[DOI]

Frank Neumann

Proceedings of the Genetic and Evolutionary Computation Conference, 2019

Dynamic parameter choices in evolutionary computation.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

Hyper-parameter tuning for the (1 + (λ, λ)) GA.

[BibT_eX]

[DOI]

Nguyen Dang

Proceedings of the Genetic and Evolutionary Computation Conference, 2019

Bayesian performance analysis for black-box optimization benchmarking.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

Interpolating Local and Global Search by Controlling the Variance of Standard Bit Mutation.

[BibT_eX]

[DOI]

Proceedings of the IEEE Congress on Evolutionary Computation, 2019

2018

Towards a More Practice-Aware Runtime Analysis of Evolutionary Algorithms.

[BibT_eX]

[DOI]

Eduardo Carvalho Pinto

CoRR, 2018

IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics.

[BibT_eX]

[DOI]

CoRR, 2018

Theory of Parameter Control for Discrete Black-Box Optimization: Provable Performance Gains Through Dynamic Parameter Choices.

[BibT_eX]

[DOI]

CoRR, 2018

On the Effectiveness of Simple Success-Based Parameter Selection Mechanisms for Two Classical Discrete Black-Box Optimization Benchmark Problems.

[BibT_eX]

[DOI]

Markus Wagner

CoRR, 2018

Complexity Theory for Discrete Black-Box Optimization Heuristics.

[BibT_eX]

[DOI]

CoRR, 2018

The (1+1) Elitist Black-Box Complexity of LeadingOnes.

[BibT_eX]

[DOI]

Algorithmica, 2018

Static and Self-Adjusting Mutation Strengths for Multi-valued Decision Variables.

[BibT_eX]

[DOI]

Algorithmica, 2018

Optimal Static and Self-Adjusting Parameter Choices for the (1+(λ, λ)) Genetic Algorithm.

[BibT_eX]

[DOI]

Algorithmica, 2018

Towards an Adaptive CMA-ES Configurator.

[BibT_eX]

[DOI]

Sander van Rijn

Proceedings of the Parallel Problem Solving from Nature - PPSN XV, 2018

A Simple Proof for the Usefulness of Crossover in Black-Box Optimization.

[BibT_eX]

[DOI]

Eduardo Carvalho Pinto

Proceedings of the Parallel Problem Solving from Nature - PPSN XV, 2018

Tutorials at PPSN 2018.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XV, 2018

Sensitivity of Parameter Control Mechanisms with Respect to Their Initialization.

[BibT_eX]

[DOI]

Markus Wagner

Proceedings of the Parallel Problem Solving from Nature - PPSN XV, 2018

Compiling a benchmarking test-suite for combinatorial black-box optimization: a position paper.

[BibT_eX]

[DOI]

Ofer M. Shir

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018

Discrepancy-based evolutionary diversity optimization.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2018

Towards a theory-guided benchmarking suite for discrete black-box optimization heuristics: profiling (1 + λ) EA variants on onemax and leadingones.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2018

Simple on-the-fly parameter selection mechanisms for two classical discrete black-box optimization benchmark problems.

[BibT_eX]

[DOI]

Markus Wagner

Proceedings of the Genetic and Evolutionary Computation Conference, 2018

2017

Introducing Elitist Black-Box Models: When Does Elitist Behavior Weaken the Performance of Evolutionary Algorithms?

[BibT_eX]

[DOI]

Evol. Comput., 2017

Theory of Randomized Optimization Heuristics (Dagstuhl Seminar 17191).

[BibT_eX]

[DOI]

Dagstuhl Reports, 2017

OneMax in Black-Box Models with Several Restrictions.

[BibT_eX]

[DOI]

Algorithmica, 2017

Preface to the Special Issue on Theory of Genetic and Evolutionary Computation.

[BibT_eX]

[DOI]

Francisco Chicano

Algorithmica, 2017

Unknown solution length problems with no asymptotically optimal run time.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2017

Non-static parameter choices in evolutionary computation.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2017

2016

Rumor spreading in random evolving graphs.

[BibT_eX]

[DOI]

Random Struct. Algorithms, 2016

Playing Mastermind With Many Colors.

[BibT_eX]

[DOI]

J. ACM, 2016

Simple and optimal randomized fault-tolerant rumor spreading.

[BibT_eX]

[DOI]

Distributed Comput., 2016

The Impact of Random Initialization on the Runtime of Randomized Search Heuristics.

[BibT_eX]

[DOI]

Algorithmica, 2016

k-Bit Mutation with Self-Adjusting k Outperforms Standard Bit Mutation.

[BibT_eX]

[DOI]

Jing Yang

Proceedings of the Parallel Problem Solving from Nature - PPSN XIV, 2016

Provably Optimal Self-adjusting Step Sizes for Multi-valued Decision Variables.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XIV, 2016

Tutorials at PPSN 2016.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XIV, 2016

Women@GECCO 2016 Chairs' Welcome.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2016

The Right Mutation Strength for Multi-Valued Decision Variables.

[BibT_eX]

[DOI]

Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, Denver, CO, USA, July 20, 2016

Theory for Non-Theoreticians.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2016

2015

From black-box complexity to designing new genetic algorithms.

[BibT_eX]

[DOI]

Franziska Ebel

Theor. Comput. Sci., 2015

Unbiased Black-Box Complexities of Jump Functions.

[BibT_eX]

[DOI]

Evol. Comput., 2015

Introducing Elitist Black-Box Models: When Does Elitist Selection Weaken the Performance of Evolutionary Algorithms?

[BibT_eX]

[DOI]

Axel de Perthuis de Laillevault

CoRR, 2015

Money for Nothing: Speeding Up Evolutionary Algorithms Through Better Initialization.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2015

Elitist Black-Box Models: Analyzing the Impact of Elitist Selection on the Performance of Evolutionary Algorithms.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2015

A Tight Runtime Analysis of the (1+(λ, λ)) Genetic Algorithm on OneMax.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2015

Optimal Parameter Choices Through Self-Adjustment: Applying the 1/5-th Rule in Discrete Settings.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2015

2014

The Price of Anarchy for Selfish Ring Routing is Two.

[BibT_eX]

[DOI]

ACM Trans. Economics and Comput., 2014

Reducing the arity in unbiased black-box complexity.

[BibT_eX]

[DOI]

Theor. Comput. Sci., 2014

Playing Mastermind with Constant-Size Memory.

[BibT_eX]

[DOI]

Theory Comput. Syst., 2014

Computing Minimum Cycle Bases in Weighted Partial 2-Trees in Linear Time.

[BibT_eX]

[DOI]

G. Ramakrishna

Jens M. Schmidt

J. Graph Algorithms Appl., 2014

Ranking-Based Black-Box Complexity.

[BibT_eX]

[DOI]

Algorithmica, 2014

The unbiased black-box complexity of partition is polynomial.

[BibT_eX]

[DOI]

Artif. Intell., 2014

Women@GECCO 2014.

[BibT_eX]

[DOI]

Una-May O'Reilly

Anna Esparcia-Alcázar

Proceedings of the Genetic and Evolutionary Computation Conference, 2014

Unbiased black-box complexities of jump functions: how to cross large plateaus.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2014

2013

Black-box complexities of combinatorial problems.

[BibT_eX]

[DOI]

Theor. Comput. Sci., 2013

Direction-reversing quasi-random rumor spreading with restarts.

[BibT_eX]

[DOI]

Inf. Process. Lett., 2013

Mutation Rate Matters Even When Optimizing Monotonic Functions.

[BibT_eX]

[DOI]

Evol. Comput., 2013

Collecting Coupons with Random Initial Stake.

[BibT_eX]

[DOI]

CoRR, 2013

Constructing low star discrepancy point sets with genetic algorithms.

[BibT_eX]

[DOI]

François-Michel De Rainville

Proceedings of the Genetic and Evolutionary Computation Conference, 2013

Lessons from the black-box: fast crossover-based genetic algorithms.

[BibT_eX]

[DOI]

Franziska Ebel

Proceedings of the Genetic and Evolutionary Computation Conference, 2013

Black-box complexity: from complexity theory to playing mastermind.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2013

Rumor Spreading in Random Evolving Graphs.

[BibT_eX]

[DOI]

Proceedings of the Algorithms - ESA 2013, 2013

The Query Complexity of Finding a Hidden Permutation.

[BibT_eX]

[DOI]

Proceedings of the Space-Efficient Data Structures, 2013

2012

Non-existence of linear universal drift functions.

[BibT_eX]

[DOI]

Daniel Johannsen

Theor. Comput. Sci., 2012

A New Randomized Algorithm to Approximate the Star Discrepancy Based on Threshold Accepting.

[BibT_eX]

[DOI]

Michael Gnewuch

Magnus Wahlström

SIAM J. Numer. Anal., 2012

Memory-restricted black-box complexity of OneMax.

[BibT_eX]

[DOI]

Inf. Process. Lett., 2012

The Deterministic and Randomized Query Complexity of a Simple Guessing Game.

[BibT_eX]

[DOI]

Electron. Colloquium Comput. Complex., 2012

Black-Box Complexity: Breaking the $O(n \log n)$ Barrier of LeadingOnes

[BibT_eX]

[DOI]

CoRR, 2012

Fast Fault Tolerant Rumor Spreading with Minimum Message Complexity

[BibT_eX]

[DOI]

CoRR, 2012

Multiplicative Drift Analysis.

[BibT_eX]

[DOI]

Daniel Johannsen

Algorithmica, 2012

2011

Memory-Restricted Black-Box Complexity.

[BibT_eX]

[DOI]

Electron. Colloquium Comput. Complex., 2011

A Randomized Algorithm Based on Threshold Accepting to Approximate the Star Discrepancy

[BibT_eX]

[DOI]

Michael Gnewuch

Magnus Wahlström

CoRR, 2011

Entwicklung einer Komplexitätstheorie für randomisierte Suchheuristiken: Black-Box-Modelle.

[BibT_eX]

[DOI]

Proceedings of the Ausgezeichnete Informatikdissertationen 2011, 2011

Too fast unbiased black-box algorithms.

[BibT_eX]

[DOI]

Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, 2011

Faster black-box algorithms through higher arity operators.

[BibT_eX]

[DOI]

Proceedings of the Foundations of Genetic Algorithms, 11th International Workshop, 2011

Towards a Complexity Theory of Randomized Search Heuristics: Ranking-Based Black-Box Complexity.

[BibT_eX]

[DOI]

Proceedings of the Computer Science - Theory and Applications, 2011

Black-Box Complexity: Breaking the O(n logn) Barrier of LeadingOnes.

[BibT_eX]

[DOI]

Proceedings of the Artificial Evolution, 2011

Toward a complexity theory for randomized search heuristics.

[BibT_eX]

[DOI]

PhD thesis, 2011

2010

Optimizing Monotone Functions Can Be Difficult.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature, 2010

Drift analysis and linear functions revisited.

[BibT_eX]

[DOI]

Daniel Johannsen

Proceedings of the IEEE Congress on Evolutionary Computation, 2010

2009

Finding optimal volume subintervals with k points and calculating the star discrepancy are NP-hard problems.

[BibT_eX]

[DOI]

Michael Gnewuch

Anand Srivastav