On the importance of diversity maintenance in estimation of distribution algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
The correlation-triggered adaptive variance scaling IDEA
Proceedings of the 8th annual conference on Genetic and evolutionary computation
SDR: a better trigger for adaptive variance scaling in normal EDAs
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Truncation selection and Gaussian EDA: bounds for sustainable progress in high-dimensional spaces
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Comparison of cauchy EDA and BIPOP-CMA-ES algorithms on the BBOB noiseless testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Comparison of cauchy EDA and pPOEMS algorithms on the BBOB noiseless testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Comparison of cauchy EDA and rosenbrock's algorithms on the BBOB noiseless testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Comparison of cauchy EDA and G3PCX algorithms on the BBOB noiseless testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
A review on probabilistic graphical models in evolutionary computation
Journal of Heuristics
Experimental comparison of six population-based algorithms for continuous black box optimization
Evolutionary Computation
The continuous differential ant-stigmergy algorithm for numerical optimization
Computational Optimization and Applications
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The restarted estimation of distribution algorithm (EDA) with Cauchy distribution as the probabilistic model is tested on the BBOB 2009 testbed. These tests prove that when using the Cauchy distribution and suitably chosen variance enlargment factor, the algorithm is usable for broad range of fitness landscapes, which is not the case for EDA with Gaussian distribution which converges prematurely. The results of the algorithm are of mixed quality and its scaling is at least quadratic.