1 citations · 1 across the 2 of their papers we have counts for
6 papers
Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection
Furong Ye, Carola Doerr, Thomas Bäck
A key challenge in the application of evolutionary algorithms in practice is the selection of an algorithm instance that best suits the problem at hand. What complicates this decis…
Benchmarking a Genetic Algorithm with Configurable Crossover Probability
Furong Ye, Hao Wang, Carola Doerr +1
We investigate a family of Genetic Algorithms (GAs) which creates offspring either from mutation or by recombining two randomly chosen parents. By scaling the crossover pro…
Benchmarking Discrete Optimization Heuristics with IOHprofiler
Carola Doerr, Furong Ye, Naama Horesh +3
Automated benchmarking environments aim to support researchers in understanding how different algorithms perform on different types of optimization problems. Such comparisons provi…
Interpolating Local and Global Search by Controlling the Variance of Standard Bit Mutation
Furong Ye, Carola Doerr, Thomas Bäck
A key property underlying the success of evolutionary algorithms (EAs) is their global search behavior, which allows the algorithms to `jump' from a current state to other parts of…
IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics
Carola Doerr, Hao Wang, Furong Ye +2
IOHprofiler is a new tool for analyzing and comparing iterative optimization heuristics. Given as input algorithms and problems written in C or Python, it provides as output a stat…
Towards a Theory-Guided Benchmarking Suite for Discrete Black-Box Optimization Heuristics: Profiling EA Variants on OneMax and LeadingOnes
Carola Doerr, Furong Ye, Sander van Rijn +2
Theoretical and empirical research on evolutionary computation methods complement each other by providing two fundamentally different approaches towards a better understanding of b…