9 citations · 10 across the 4 of their papers we have counts for
4 papers · 1 filter
A Crowding Distance That Provably Solves the Difficulties of the NSGA-II in Many-Objective Optimization
Weijie Zheng, Yao Gao, Benjamin Doerr
Recent theoretical works have shown that the NSGA-II can have enormous difficulties to solve problems with more than two objectives. In contrast, algorithms like the NSGA-III or SM…
Hyper-Heuristics Can Profit From Global Variation Operators
Benjamin Doerr, Johannes F. Lutzeyer
In recent work, Lissovoi, Oliveto, and Warwicker (Artificial Intelligence (2023)) proved that the Move Acceptance Hyper-Heuristic (MAHH) leaves the local optimum of the multimodal…
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…