
Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance
Finding the best configuration of algorithms' hyperparameters for a give...
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OPTION: OPTImization Algorithm Benchmarking ONtology
Many platforms for benchmarking optimization algorithms offer users the ...
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Personalizing Performance Regression Models to BlackBox Optimization Problems
Accurately predicting the performance of different optimization algorith...
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The Impact of HyperParameter Tuning for LandscapeAware Performance Regression and Algorithm Selection
Automated algorithm selection and configuration methods that build on ex...
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Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules
Introducing new algorithmic ideas is a key part of the continuous improv...
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Blending Dynamic Programming with Monte Carlo Simulation for Bounding the Running Time of Evolutionary Algorithms
With the goal to provide absolute lower bounds for the best possible run...
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Leveraging Benchmarking Data for Informed OneShot Dynamic Algorithm Selection
A key challenge in the application of evolutionary algorithms in practic...
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Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking Frameworks
We present a first proofofconcept usecase that demonstrates the effic...
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Towards FeatureBased Performance Regression Using Trajectory Data
Blackbox optimization is a very active area of research, with many new ...
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Optimal Static Mutation Strength Distributions for the (1+λ) Evolutionary Algorithm on OneMax
Most evolutionary algorithms have parameters, which allow a great flexib...
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Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions
Facilitated by the recent advances of Machine Learning (ML), the automat...
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Star Discrepancy Subset Selection: Problem Formulation and Efficient Approaches for Low Dimensions
Motivated by applications in instance selection, we introduce the star d...
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Squirrel: A Switching Hyperparameter Optimizer
In this short note, we describe our submission to the NeurIPS 2020 BBO c...
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BlackBox Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking
Existing studies in blackbox optimization suffer from low generalizabil...
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Linear Matrix Factorization Embeddings for Singleobjective Optimization Landscapes
Automated perinstance algorithm selection and configuration have shown ...
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IOHanalyzer: Performance Analysis for Iterative Optimization Heuristic
We propose IOHanalyzer, a new software for analyzing the empirical perfo...
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Benchmarking in Optimization: Best Practice and Open Issues
This survey compiles ideas and recommendations from more than a dozen re...
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High Dimensional Bayesian Optimization Assisted by Principal Component Analysis
Bayesian Optimization (BO) is a surrogateassisted global optimization t...
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Optimal Mutation Rates for the (1+λ) EA on OneMax
The OneMax problem, alternatively known as the Hamming distance problem,...
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Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy
Exploratory landscape analysis (ELA) supports supervised learning approa...
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Hybridizing the 1/5th Success Rule with QLearning for Controlling the Mutation Rate of an Evolutionary Algorithm
It is well known that evolutionary algorithms (EAs) achieve peak perform...
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LandscapeAware FixedBudget Performance Regression and Algorithm Selection for Modular CMAES Variants
Automated algorithm selection promises to support the user in the decisi...
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Towards Dynamic Algorithm Selection for Numerical BlackBox Optimization: Investigating BBOB as a Use Case
One of the most challenging problems in evolutionary computation is to s...
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Benchmarking a (μ+λ) Genetic Algorithm with Configurable Crossover Probability
We investigate a family of (μ+λ) Genetic Algorithms (GAs) which creates ...
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MATE: A Modelbased Algorithm Tuning Engine
In this paper, we introduce a Modelbased Algorithm Turning Engine, name...
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Variance Reduction for Better Sampling in Continuous Domains
Design of experiments, random search, initialization of populationbased...
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FixedTarget Runtime Analysis
Runtime analysis aims at contributing to our understanding of evolutiona...
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Initial Design Strategies and their Effects on Sequential ModelBased Optimization
Sequential modelbased optimization (SMBO) approaches are algorithms for...
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Benchmarking Discrete Optimization Heuristics with IOHprofiler
Automated benchmarking environments aim to support researchers in unders...
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OneShot DecisionMaking with and without Surrogates
Oneshot decision making is required in situations in which we can evalu...
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Sequential vs. Integrated Algorithm Selection and Configuration: A Case Study for the Modular CMAES
When faced with a specific optimization problem, choosing which algorith...
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Optimization of ChanceConstrained Submodular Functions
Submodular optimization plays a key role in many realworld problems. In...
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Offspring Population Size Matters when Comparing Evolutionary Algorithms with SelfAdjusting Mutation Rates
We analyze the performance of the 2rate (1+λ) Evolutionary Algorithm (E...
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Maximizing Drift is Not Optimal for Solving OneMax
It seems very intuitive that for the maximization of the OneMax problem ...
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Online Selection of CMAES Variants
In the field of evolutionary computation, one of the most challenging to...
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HyperParameter Tuning for the (1+(λ,λ)) GA
It is known that the (1+(λ,λ)) Genetic Algorithm (GA) with selfadjustin...
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SelfAdjusting Mutation Rates with Provably Optimal Success Rules
The onefifth success rule is one of the bestknown and most widely acce...
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Fast ReOptimization via Structural Diversity
When a problem instance is perturbed by a small modification, one would ...
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Interpolating Local and Global Search by Controlling the Variance of Standard Bit Mutation
A key property underlying the success of evolutionary algorithms (EAs) i...
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The Query Complexity of a PermutationBased Variant of Mastermind
We study the query complexity of a permutationbased variant of the gues...
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Towards a More PracticeAware Runtime Analysis of Evolutionary Algorithms
Theory of evolutionary computation (EC) aims at providing mathematically...
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IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics
IOHprofiler is a new tool for analyzing and comparing iterative optimiza...
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Towards a TheoryGuided Benchmarking Suite for Discrete BlackBox Optimization Heuristics: Profiling (1+λ) EA Variants on OneMax and LeadingOnes
Theoretical and empirical research on evolutionary computation methods c...
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Optimal Parameter Choices via Precise BlackBox Analysis
It has been observed that some working principles of evolutionary algori...
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Theory of Parameter Control for Discrete BlackBox Optimization: Provable Performance Gains Through Dynamic Parameter Choices
Parameter control aims at realizing performance gains through a dynamic ...
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On the Effectiveness of Simple SuccessBased Parameter Selection Mechanisms for Two Classical Discrete BlackBox Optimization Benchmark Problems
Despite significant empirical and theoretically supported evidence that ...
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Discrepancybased Evolutionary Diversity Optimization
Diversity plays a crucial role in evolutionary computation. While divers...
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Complexity Theory for Discrete BlackBox Optimization Heuristics
A predominant topic in the theory of evolutionary algorithms and, more g...
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The Right Mutation Strength for MultiValued Decision Variables
The most common representation in evolutionary computation are bit strin...
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The (1+1) Elitist BlackBox Complexity of LeadingOnes
One important goal of blackbox complexity theory is the development of ...
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Carola Doerr
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Postdoctoral researcher at the Max Planck Institute for Informatics