Carola Doerr

Affiliations:
  • Sorbonne Université, CNRS, LIP6, Paris, France
  • Max Planck Institute for Informatics, Saarbrücken, Germany (former)


According to our database1, Carola Doerr authored at least 163 papers between 2009 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of two.

Timeline

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Online presence:

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Bibliography

2023
Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms.
CoRR, 2023

RF+clust for Leave-One-Problem-Out Performance Prediction.
CoRR, 2023

2022
IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics.
ACM Trans. Evol. Learn. Optim., 2022

Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance.
IEEE Trans. Evol. Comput., 2022

Black-Box Optimization Revisited: Improving Algorithm Selection Wizards Through Massive Benchmarking.
IEEE Trans. Evol. Comput., 2022

Guest Editorial Special Issue on Benchmarking Sampling-Based Optimization Heuristics: Methodology and Software.
IEEE Trans. Evol. Comput., 2022

Star discrepancy subset selection: Problem formulation and efficient approaches for low dimensions.
J. Complex., 2022

Explainable Model-specific Algorithm Selection for Multi-Label Classification.
CoRR, 2022

Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis.
CoRR, 2022

PI is back! Switching Acquisition Functions in Bayesian Optimization.
CoRR, 2022

Run Time Analysis for Random Local Search on Generalized Majority Functions.
CoRR, 2022

Chaining of Numerical Black-box Algorithms: Warm-Starting and Switching Points.
CoRR, 2022

Non-Elitist Selection among Survivor Configurations can Improve the Performance of Irace.
CoRR, 2022

Fixed-Target Runtime Analysis.
Algorithmica, 2022

Non-elitist Selection Can Improve the Performance of Irace.
Proceedings of the Parallel Problem Solving from Nature - PPSN XVII, 2022

Improving Nevergrad's Algorithm Selection Wizard NGOpt Through Automated Algorithm Configuration.
Proceedings of the Parallel Problem Solving from Nature - PPSN XVII, 2022

Per-run Algorithm Selection with Warm-Starting Using Trajectory-Based Features.
Proceedings of the Parallel Problem Solving from Nature - PPSN XVII, 2022

High Dimensional Bayesian Optimization with Kernel Principal Component Analysis.
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.
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.
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.
Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Boston, Massachusetts, USA, July 9, 2022

Automated algorithm selection for radar network configuration.
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.
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.
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.
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.
Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Boston, Massachusetts, USA, July 9, 2022

Trajectory-based Algorithm Selection with Warm-starting.
Proceedings of the IEEE Congress on Evolutionary Computation, 2022

Fast Re-Optimization of LeadingOnes with Frequent Changes.
Proceedings of the IEEE Congress on Evolutionary Computation, 2022

2021
Maximizing Drift Is Not Optimal for Solving OneMax.
Evol. Comput., 2021

IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics.
CoRR, 2021

Optimal Static Mutation Strength Distributions for the (1+λ) Evolutionary Algorithm on OneMax.
CoRR, 2021

Star Discrepancy Subset Selection: Problem Formulation and Efficient Approaches for Low Dimensions.
CoRR, 2021

Self-Adjusting Mutation Rates with Provably Optimal Success Rules.
Algorithmica, 2021

Leveraging benchmarking data for informed one-shot dynamic algorithm selection.
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.
Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

OPTION: optimization algorithm benchmarking ontology.
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.
Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

Personalizing performance regression models to black-box optimization problems.
Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

Optimal static mutation strength distributions for the (1 <i>+ λ</i>) evolutionary algorithm on OneMax.
Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

Towards large scale automated algorithm design by integrating modular benchmarking frameworks.
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.
Proceedings of the Evolutionary Computation in Combinatorial Optimization, 2021

Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions.
Proceedings of the Applications of Evolutionary Computation, 2021

Towards Feature-Based Performance Regression Using Trajectory Data.
Proceedings of the Applications of Evolutionary Computation, 2021

Blending Dynamic Programming with Monte Carlo Simulation for Bounding the Running Time of Evolutionary Algorithms.
Proceedings of the IEEE Congress on Evolutionary Computation, 2021

2020
Theory of Parameter Control for Discrete Black-Box Optimization: Provable Performance Gains Through Dynamic Parameter Choices.
Proceedings of the Theory of Evolutionary Computation, 2020

Complexity Theory for Discrete Black-Box Optimization Heuristics.
Proceedings of the Theory of Evolutionary Computation, 2020

Optimal parameter choices via precise black-box analysis.
Theor. Comput. Sci., 2020

Squirrel: A Switching Hyperparameter Optimizer.
CoRR, 2020

IOHanalyzer: Performance Analysis for Iterative Optimization Heuristic.
CoRR, 2020

Benchmarking in Optimization: Best Practice and Open Issues.
CoRR, 2020

MATE: A Model-based Algorithm Tuning Engine.
CoRR, 2020

Initial Design Strategies and their Effects on Sequential Model-Based Optimization.
CoRR, 2020

Benchmarking discrete optimization heuristics with IOHprofiler.
Appl. Soft Comput., 2020

Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapes.
Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence, 2020

Benchmarking a (μ +λ ) Genetic Algorithm with Configurable Crossover Probability.
Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy.
Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

High Dimensional Bayesian Optimization Assisted by Principal Component Analysis.
Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

Variance Reduction for Better Sampling in Continuous Domains.
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.
Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

Optimal Mutation Rates for the (1+λ ) EA on OneMax.
Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

Evolving Sampling Strategies for One-Shot Optimization Tasks.
Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

Benchmarking and analyzing iterative optimization heuristics with IOHprofiler.
Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

Integrated vs. sequential approaches for selecting and tuning CMA-ES variants.
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.
Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

Dynamic control parameter choices in evolutionary computation: GECCO 2020 tutorial.
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.
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.
Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

Optimization of Chance-Constrained Submodular Functions.
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.
, 2020

2019
The query complexity of a permutation-based variant of Mastermind.
Discret. Appl. Math., 2019

Theory of Randomized Optimization Heuristics (Dagstuhl Reports 19431).
Dagstuhl Reports, 2019

One-Shot Decision-Making with and without Surrogates.
CoRR, 2019

Sequential vs. Integrated Algorithm Selection and Configuration: A Case Study for the Modular CMA-ES.
CoRR, 2019

Hyper-Parameter Tuning for the (1+(λ, λ)) GA.
CoRR, 2019

Preface to the Special Issue on Theory of Genetic and Evolutionary Computation.
Algorithmica, 2019

Solving Problems with Unknown Solution Length at Almost No Extra Cost.
Algorithmica, 2019

Fixed-target runtime analysis of the (1 + 1) EA with resampling.
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

Online selection of CMA-ES variants.
Proceedings of the Genetic and Evolutionary Computation Conference, 2019

Offspring population size matters when comparing evolutionary algorithms with self-adjusting mutation rates.
Proceedings of the Genetic and Evolutionary Computation Conference, 2019

Expressiveness and robustness of landscape features.
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

Adaptive landscape analysis.
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.
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

Coupling the design of benchmark with algorithm in landscape-aware solver design.
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

Making a case for (Hyper-)parameter tuning as benchmark problems.
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

Fast re-optimization via structural diversity.
Proceedings of the Genetic and Evolutionary Computation Conference, 2019

Hyper-parameter tuning for the (1 + (<i>λ, λ</i>)) GA.
Proceedings of the Genetic and Evolutionary Computation Conference, 2019

Bayesian performance analysis for black-box optimization benchmarking.
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

Interpolating Local and Global Search by Controlling the Variance of Standard Bit Mutation.
Proceedings of the IEEE Congress on Evolutionary Computation, 2019

2018
Towards a More Practice-Aware Runtime Analysis of Evolutionary Algorithms.
CoRR, 2018

IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics.
CoRR, 2018

Theory of Parameter Control for Discrete Black-Box Optimization: Provable Performance Gains Through Dynamic Parameter Choices.
CoRR, 2018

On the Effectiveness of Simple Success-Based Parameter Selection Mechanisms for Two Classical Discrete Black-Box Optimization Benchmark Problems.
CoRR, 2018

Complexity Theory for Discrete Black-Box Optimization Heuristics.
CoRR, 2018

The (1+1) Elitist Black-Box Complexity of LeadingOnes.
Algorithmica, 2018

Static and Self-Adjusting Mutation Strengths for Multi-valued Decision Variables.
Algorithmica, 2018

Optimal Static and Self-Adjusting Parameter Choices for the (1+(λ, λ)) Genetic Algorithm.
Algorithmica, 2018

Towards an Adaptive CMA-ES Configurator.
Proceedings of the Parallel Problem Solving from Nature - PPSN XV, 2018

A Simple Proof for the Usefulness of Crossover in Black-Box Optimization.
Proceedings of the Parallel Problem Solving from Nature - PPSN XV, 2018


Sensitivity of Parameter Control Mechanisms with Respect to Their Initialization.
Proceedings of the Parallel Problem Solving from Nature - PPSN XV, 2018

Compiling a benchmarking test-suite for combinatorial black-box optimization: a position paper.
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018

Discrepancy-based evolutionary diversity optimization.
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.
Proceedings of the Genetic and Evolutionary Computation Conference, 2018

Dynamic parameter choices in evolutionary computation.
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018

Simple on-the-fly parameter selection mechanisms for two classical discrete black-box optimization benchmark problems.
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?
Evol. Comput., 2017

Theory of Randomized Optimization Heuristics (Dagstuhl Seminar 17191).
Dagstuhl Reports, 2017

OneMax in Black-Box Models with Several Restrictions.
Algorithmica, 2017

Preface to the Special Issue on Theory of Genetic and Evolutionary Computation.
Algorithmica, 2017

Unknown solution length problems with no asymptotically optimal run time.
Proceedings of the Genetic and Evolutionary Computation Conference, 2017

Non-static parameter choices in evolutionary computation.
Proceedings of the Genetic and Evolutionary Computation Conference, 2017

2016
Rumor spreading in random evolving graphs.
Random Struct. Algorithms, 2016

Playing Mastermind With Many Colors.
J. ACM, 2016

Simple and optimal randomized fault-tolerant rumor spreading.
Distributed Comput., 2016

The Impact of Random Initialization on the Runtime of Randomized Search Heuristics.
Algorithmica, 2016

k-Bit Mutation with Self-Adjusting k Outperforms Standard Bit Mutation.
Proceedings of the Parallel Problem Solving from Nature - PPSN XIV, 2016

Provably Optimal Self-adjusting Step Sizes for Multi-valued Decision Variables.
Proceedings of the Parallel Problem Solving from Nature - PPSN XIV, 2016


Women@GECCO 2016 Chairs' Welcome.
Proceedings of the Genetic and Evolutionary Computation Conference, 2016

The Right Mutation Strength for Multi-Valued Decision Variables.
Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, Denver, CO, USA, July 20, 2016

Theory for Non-Theoreticians.
Proceedings of the Genetic and Evolutionary Computation Conference, 2016

2015
From black-box complexity to designing new genetic algorithms.
Theor. Comput. Sci., 2015

Unbiased Black-Box Complexities of Jump Functions.
Evol. Comput., 2015

Introducing Elitist Black-Box Models: When Does Elitist Selection Weaken the Performance of Evolutionary Algorithms?
CoRR, 2015

Money for Nothing: Speeding Up Evolutionary Algorithms Through Better Initialization.
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.
Proceedings of the Genetic and Evolutionary Computation Conference, 2015

A Tight Runtime Analysis of the (1+(λ, λ)) Genetic Algorithm on OneMax.
Proceedings of the Genetic and Evolutionary Computation Conference, 2015

Optimal Parameter Choices Through Self-Adjustment: Applying the 1/5-th Rule in Discrete Settings.
Proceedings of the Genetic and Evolutionary Computation Conference, 2015

2014
The Price of Anarchy for Selfish Ring Routing is Two.
ACM Trans. Economics and Comput., 2014

Reducing the arity in unbiased black-box complexity.
Theor. Comput. Sci., 2014

Playing Mastermind with Constant-Size Memory.
Theory Comput. Syst., 2014

Computing Minimum Cycle Bases in Weighted Partial 2-Trees in Linear Time.
J. Graph Algorithms Appl., 2014

Ranking-Based Black-Box Complexity.
Algorithmica, 2014

The unbiased black-box complexity of partition is polynomial.
Artif. Intell., 2014

Women@GECCO 2014.
Proceedings of the Genetic and Evolutionary Computation Conference, 2014

Unbiased black-box complexities of jump functions: how to cross large plateaus.
Proceedings of the Genetic and Evolutionary Computation Conference, 2014

2013
Black-box complexities of combinatorial problems.
Theor. Comput. Sci., 2013

Direction-reversing quasi-random rumor spreading with restarts.
Inf. Process. Lett., 2013

Mutation Rate Matters Even When Optimizing Monotonic Functions.
Evol. Comput., 2013

Collecting Coupons with Random Initial Stake.
CoRR, 2013

Constructing low star discrepancy point sets with genetic algorithms.
Proceedings of the Genetic and Evolutionary Computation Conference, 2013

Lessons from the black-box: fast crossover-based genetic algorithms.
Proceedings of the Genetic and Evolutionary Computation Conference, 2013

Black-box complexity: from complexity theory to playing mastermind.
Proceedings of the Genetic and Evolutionary Computation Conference, 2013

Rumor Spreading in Random Evolving Graphs.
Proceedings of the Algorithms - ESA 2013, 2013

The Query Complexity of Finding a Hidden Permutation.
Proceedings of the Space-Efficient Data Structures, 2013

2012
Non-existence of linear universal drift functions.
Theor. Comput. Sci., 2012

A New Randomized Algorithm to Approximate the Star Discrepancy Based on Threshold Accepting.
SIAM J. Numer. Anal., 2012

Memory-restricted black-box complexity of OneMax.
Inf. Process. Lett., 2012

The Deterministic and Randomized Query Complexity of a Simple Guessing Game.
Electron. Colloquium Comput. Complex., 2012

Black-Box Complexity: Breaking the $O(n \log n)$ Barrier of LeadingOnes
CoRR, 2012

Fast Fault Tolerant Rumor Spreading with Minimum Message Complexity
CoRR, 2012

Multiplicative Drift Analysis.
Algorithmica, 2012

2011
Memory-Restricted Black-Box Complexity.
Electron. Colloquium Comput. Complex., 2011

A Randomized Algorithm Based on Threshold Accepting to Approximate the Star Discrepancy
CoRR, 2011

Entwicklung einer Komplexitätstheorie für randomisierte Suchheuristiken: Black-Box-Modelle.
Proceedings of the Ausgezeichnete Informatikdissertationen 2011, 2011

Too fast unbiased black-box algorithms.
Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, 2011

Faster black-box algorithms through higher arity operators.
Proceedings of the Foundations of Genetic Algorithms, 11th International Workshop, 2011

Towards a Complexity Theory of Randomized Search Heuristics: Ranking-Based Black-Box Complexity.
Proceedings of the Computer Science - Theory and Applications, 2011

Black-Box Complexity: Breaking the O(n logn) Barrier of LeadingOnes.
Proceedings of the Artificial Evolution, 2011

Toward a complexity theory for randomized search heuristics.
PhD thesis, 2011

2010
Optimizing Monotone Functions Can Be Difficult.
Proceedings of the Parallel Problem Solving from Nature, 2010

Drift analysis and linear functions revisited.
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.
J. Complex., 2009


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