no code implementations • 10 Apr 2020 • Benjamin Doerr, Martin Krejca
With elementary means, we prove a stronger run time guarantee for the univariate marginal distribution algorithm (UMDA) optimizing the LeadingOnes benchmark function in the desirable regime with low genetic drift.
no code implementations • 10 Jul 2018 • Benjamin Doerr, Martin Krejca
Estimation-of-distribution algorithms (EDAs) are randomized search heuristics that create a probabilistic model of the solution space, which is updated iteratively, based on the quality of the solutions sampled according to the model.
no code implementations • 10 Feb 2015 • Tobias Friedrich, Timo Kötzing, Martin Krejca, Andrew M. Sutton
For this, we model sexual recombination with a simple estimation of distribution algorithm called the Compact Genetic Algorithm (cGA), which we compare with the classical $\mu+1$ EA.
