55 citations · 92 across the 5 of their papers we have counts for
6 papers
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…
Stagnation Detection Meets Fast Mutation
Benjamin Doerr, Amirhossein Rajabi
Two mechanisms have recently been proposed that can significantly speed up finding distant improving solutions via mutation, namely using a random mutation rate drawn from a heavy-…
Stagnation Detection in Highly Multimodal Fitness Landscapes
Amirhossein Rajabi, Carsten Witt
Stagnation detection has been proposed as a mechanism for randomized search heuristics to escape from local optima by automatically increasing the size of the neighborhood to find…
Stagnation Detection with Randomized Local Search
Amirhossein Rajabi, Carsten Witt
Recently a mechanism called stagnation detection was proposed that automatically adjusts the mutation rate of evolutionary algorithms when they encounter local optima. The so-calle…
Evolutionary Algorithms with Self-adjusting Asymmetric Mutation
Amirhossein Rajabi, Carsten Witt
Evolutionary Algorithms (EAs) and other randomized search heuristics are often considered as unbiased algorithms that are invariant with respect to different transformations of the…
Self-Adjusting Evolutionary Algorithms for Multimodal Optimization
Amirhossein Rajabi, Carsten Witt
Recent theoretical research has shown that self-adjusting and self-adaptive mechanisms can provably outperform static settings in evolutionary algorithms for binary search spaces.…