Furong Ye
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View article: Automatically discovering heuristics in a complex SAT solver with large language models
Automatically discovering heuristics in a complex SAT solver with large language models Open
Satisfiability problem (SAT) is a cornerstone of computational complexity with broad industrial applications, and it remains challenging to optimize modern SAT solvers in real-world settings due to their intricate architectures. While auto…
View article: Better Understandings and Configurations in MaxSAT Stochastic Local Search Solvers via Anytime Performance Analysis
Better Understandings and Configurations in MaxSAT Stochastic Local Search Solvers via Anytime Performance Analysis Open
Though numerous solvers have been proposed for the MaxSAT problem, and the benchmark environment such as MaxSAT Evaluations provides a platform for the comparison of the state-of-the-art solvers, existing assessments were usually evaluated…
View article: Bi-Objective Contract Allocation for Guaranteed Delivery Advertising
Bi-Objective Contract Allocation for Guaranteed Delivery Advertising Open
Contemporary systems of Guaranteed Delivery (GD) advertising work with two different stages, namely, the offline selling stage and the online serving stage. The former deals with contract allocation, and the latter fulfills the impression …
View article: What Performance Indicators to Use for Self-Adaptation in Multi-Objective Evolutionary Algorithms
What Performance Indicators to Use for Self-Adaptation in Multi-Objective Evolutionary Algorithms Open
Algorithms and the Foundations of Software technology
View article: MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts
MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts Open
Choosing a set of benchmark problems is often a key component of any empirical evaluation of iterative optimization heuristics. In continuous, single-objective optimization, several sets of problems have become widespread, including the we…
View article: MA-BBOB - Reproducibility and Additional Data
MA-BBOB - Reproducibility and Additional Data Open
Reproducibility files for the paper: MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts This document details the reproduction steps for the paper "MA-BBOB: A Problem Generator for Black-Box Optimi…
View article: Better Understandings and Configurations in MaxSAT Local Search Solvers via Anytime Performance Analysis
Better Understandings and Configurations in MaxSAT Local Search Solvers via Anytime Performance Analysis Open
Though numerous solvers have been proposed for the MaxSAT problem, and the benchmark environment such as MaxSAT Evaluations provides a platform for the comparison of the state-of-the-art solvers, existing assessments were usually evaluated…
View article: Impact of spatial transformations on landscape features of CEC2022 basic benchmark problems
Impact of spatial transformations on landscape features of CEC2022 basic benchmark problems Open
When benchmarking optimization heuristics, we need to take care to avoid an algorithm exploiting biases in the construction of the used problems. One way in which this might be done is by providing different versions of each problem but wi…
View article: MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts
MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts Open
Choosing a set of benchmark problems is often a key component of any empirical evaluation of iterative optimization heuristics. In continuous, single-objective optimization, several sets of problems have become widespread, including the we…
View article: MA-BBOB - Reproducibility and Additional Data
MA-BBOB - Reproducibility and Additional Data Open
<p># Reproducability files for the paper: MABBOB extension</p>\n<p>This document details the reproduction steps for the paper "MA-BBOB: A Problem Generator for Black-Box Optimization through Affine Combinations and Shifts…
View article: General Boolean Function Benchmark Suite
General Boolean Function Benchmark Suite Open
Just over a decade ago, the first comprehensive review on the state of benchmarking in Genetic Programming (GP) analyzed the mismatch between the problems that are used to test the performance of GP systems and real-world problems. Since t…
View article: IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics
IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics Open
We present IOHexperimenter, the experimentation module of the IOHprofiler project. IOHexperimenter aims at providing an easy-to-use and customizable toolbox for benchmarking iterative optimization heuristics such as local search, evolution…
View article: Towards a General Boolean Function Benchmark Suite
Towards a General Boolean Function Benchmark Suite Open
Just over a decade ago, the first comprehensive review on the state of benchmarking in Genetic Programming (GP) analyzed the mismatch between the problems that are used to test the performance of GP systems and real-world problems. Since t…
View article: Benchmarking and analyzing iterative optimization heuristics with IOHprofiler
Benchmarking and analyzing iterative optimization heuristics with IOHprofiler Open
View article: When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems
When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems Open
The domain of an optimization problem is seen as one of its most important characteristics. In particular, the distinction between continuous and discrete optimization is rather impactful. Based on this, the optimizing algorithm, analyzing…
View article: Using Affine Combinations of BBOB Problems for Performance Assessment
Using Affine Combinations of BBOB Problems for Performance Assessment Open
Benchmarking plays a major role in the development and analysis of optimization algorithms. As such, the way in which the used benchmark problems are defined significantly affects the insights that can be gained from any given benchmark st…
View article: Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler
Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler Open
Submodular functions play a key role in the area of optimization as they allow to model many real-world problems that face diminishing returns. Evolutionary algorithms have been shown to obtain strong theoretical performance guarantees for…
View article: MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts
MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts Open
Extending a recent suggestion to generate new instances for numerical black-box optimization benchmarking by interpolating pairs of the well-established BBOB functions from the COmparing COntinuous Optimizers (COCO) platform, we propose in…
View article: When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems
When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems Open
The domain of an optimization problem is seen as one of its most important characteristics. In particular, the distinction between continuous and discrete optimization is rather impactful. Based on this, the optimizing algorithm, analyzing…
View article: When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems - Data and Code
When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems - Data and Code Open
Reproducibility files and additional figures for the paper When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems https://doi.org/10.1145/3583131.3590410
View article: Many-Affine BBOB Function Combinations - Data and Figures
Many-Affine BBOB Function Combinations - Data and Figures Open
This repository contains raw data and additional figures for the paper "Many-Affine BBOB Function Combinations". The raw data (performance of 5 algorithms) is labelled 'data.zip', while the IOHanalzyer-processed version is marked 'rds.zip'…
View article: Using Affine Combinations of BBOB Problems for Performance Assessment
Using Affine Combinations of BBOB Problems for Performance Assessment Open
Benchmarking plays a major role in the development and analysis of optimization algorithms. As such, the way in which the used benchmark problems are defined significantly affects the insights that can be gained from any given benchmark st…
View article: Using Affine Combinations of BBOB Problems for Performance Assessment - Code and Data
Using Affine Combinations of BBOB Problems for Performance Assessment - Code and Data Open
This repository contains the code and data for reproducibility of the paper 'Modular Differential Evolution'. The following files are included: - collect_data: Python files was used to access the affine function combinations and run the n…
View article: Using Affine Combinations of BBOB Problems for Performance Assessment - Code and Data
Using Affine Combinations of BBOB Problems for Performance Assessment - Code and Data Open
This repository contains the code and data for reproducibility of the paper 'Modular Differential Evolution'. The following files are included: - collect_data: Python files was used to access the affine function combinations and run the ne…
View article: Evolutionary Algorithms for Parameter Optimization—Thirty Years Later
Evolutionary Algorithms for Parameter Optimization—Thirty Years Later Open
Thirty years, 1993–2023, is a huge time frame in science. We address some major developments in the field of evolutionary algorithms, with applications in parameter optimization, over these 30 years. These include the covariance matrix ada…
View article: IOHanalyzer: Detailed performance analyses for iterative optimization heuristics
IOHanalyzer: Detailed performance analyses for iterative optimization heuristics Open
This paper summarizes our work "IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics", to appear in ACM Transactions on Evolutionary Learning and Optimization.
View article: Benchmarking and analyzing iterative optimization heuristics with IOH profiler
Benchmarking and analyzing iterative optimization heuristics with IOH profiler Open
tutorial Open Access Share on Benchmarking and analyzing iterative optimization heuristics with IOH profiler Authors: Carola Doerr View Profile , Hao Wang View Profile , Diederick Vermetten View Profile , Thomas Bäck View Profile , Jacob d…
View article: Automated configuration of genetic algorithms by tuning for anytime performance
Automated configuration of genetic algorithms by tuning for anytime performance Open
This paper summarizes our work "Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance", to appear in IEEE Transactions on Evolutionary Computation.
View article: Data Sets for the study "Non-Elitist Selection Can Improve the Performance of Irace"
Data Sets for the study "Non-Elitist Selection Can Improve the Performance of Irace" Open
This is the result of the study "Non-Elitist Selection Can Improve the Performance of Irace." Apart from the results of the three methods, i.e., irace, irace-rand, and irace-entropy, presented in the paper, the results of irace-Gower, whic…
View article: Data Sets for the study "Non-Elitist Selection Can Improve the Performance of Irace"
Data Sets for the study "Non-Elitist Selection Can Improve the Performance of Irace" Open
This is the result of the study "Non-Elitist Selection Can Improve the Performance of Irace." Apart from the results of the three methods, i.e., irace, irace-rand, and irace-entropy, presented in the paper, the results of irace-Gower, whic…