214 citations · 2k across the 102 of their papers we have counts for
115 papers
IMProofBench: Benchmarking AI on Research-Level Mathematical Proof Generation
Johannes Schmitt, Gergely Bérczi, Jasper Dekoninck +57
As the mathematical capabilities of large language models (LLMs) improve, it becomes increasingly important to evaluate their performance on research-level tasks at the frontier of…
Bivariate Estimation-of-Distribution Algorithms Can Find an Exponential Number of Optima
Benjamin Doerr, Martin S. Krejca
Finding a large set of optima in a multimodal optimization landscape is a challenging task. Classical population-based evolutionary algorithms typically converge only to a single s…
The First Proven Performance Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a Combinatorial Optimization Problem
Sacha Cerf, Benjamin Doerr, Benjamin Hebras +2
The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is one of the most prominent algorithms to solve multi-objective optimization problems. Recently, the first mathematical ru…
Runtime Analyses of Multi-Objective Evolutionary Algorithms in the Presence of Noise
Matthieu Dinot, Benjamin Doerr, Ulysse Hennebelle +1
In single-objective optimization, it is well known that evolutionary algorithms also without further adjustments can tolerate a certain amount of noise in the evaluation of the obj…
How Well Does the Metropolis Algorithm Cope With Local Optima?
Benjamin Doerr, Taha El Ghazi El Houssaini, Amirhossein Rajabi +1
The Metropolis algorithm (MA) is a classic stochastic local search heuristic. It avoids getting stuck in local optima by occasionally accepting inferior solutions. To better and in…
Larger Offspring Populations Help the Genetic Algorithm to Overcome the Noise
Alexandra Ivanova, Denis Antipov, Benjamin Doerr
Evolutionary algorithms are known to be robust to noise in the evaluation of the fitness. In particular, larger offspring population sizes often lead to strong robustness. We analy…