Furong Ye

Chuan Luo

Shaowei Cai

CoRR, 2024

Impact of spatial transformations on landscape features of CEC2022 basic benchmark problems.

[BibT_eX]

[DOI]

CoRR, 2024

2023

MA-BBOB - Reproducibility and Additional Data.

[BibT_eX]

[DOI]

Dataset, December, 2023

Evolutionary Algorithms for Parameter Optimization - Thirty Years Later.

[BibT_eX]

[DOI]

Evol. Comput., 2023

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

[BibT_eX]

[DOI]

CoRR, 2023

What Performance Indicators to Use for Self-Adaptation in Multi-Objective Evolutionary Algorithms.

[BibT_eX]

[DOI]

CoRR, 2023

Using Affine Combinations of BBOB Problems for Performance Assessment.

[BibT_eX]

[DOI]

Diederick Vermetten

Proceedings of the Genetic and Evolutionary Computation Conference, 2023

When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2023

Towards a General Boolean Function Benchmark Suite.

[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

General Boolean Function Benchmark Suite.

[BibT_eX]

[DOI]

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

Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler.

[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

2022

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

[BibT_eX]

[DOI]

Dataset, April, 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

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

[BibT_eX]

[DOI]

Diederick L. Vermetten

CoRR, 2022

Non-elitist Selection Can Improve the Performance of Irace.

[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

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

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

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

[BibT_eX]

[DOI]

Dataset, February, 2021

IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics.

[BibT_eX]

[DOI]

CoRR, 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

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

IOHanalyzer: Performance Analysis for Iterative Optimization Heuristic.

[BibT_eX]

[DOI]

CoRR, 2020

Benchmarking discrete optimization heuristics with IOHprofiler.

[BibT_eX]

[DOI]

Appl. Soft Comput., 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

2019

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

[BibT_eX]

[DOI]