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pro vyhledávání: '"univariate marginal distribution algorithm"'
Autor:
Doerr, Benjamin, Krejca, Martin S.
Publikováno v:
Evolutionary Computation (2021) 29 (4): 543-563
In their recent work, Lehre and Nguyen (FOGA 2019) show that the univariate marginal distribution algorithm (UMDA) needs time exponential in the parent populations size to optimize the DeceptiveLeadingBlocks (DLB) problem. They conclude from this res
Externí odkaz:
http://arxiv.org/abs/2007.08277
Autor:
Doerr, Benjamin, Krejca, Martin
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. If the population size is at least q
Externí odkaz:
http://arxiv.org/abs/2004.04978
We introduce a new benchmark problem called Deceptive Leading Blocks (DLB) to rigorously study the runtime of the Univariate Marginal Distribution Algorithm (UMDA) in the presence of epistasis and deception. We show that simple Evolutionary Algorithm
Externí odkaz:
http://arxiv.org/abs/1907.12438
We perform a rigorous runtime analysis for the Univariate Marginal Distribution Algorithm on the LeadingOnes function, a well-known benchmark function in the theory community of evolutionary computation with a high correlation between decision variab
Externí odkaz:
http://arxiv.org/abs/1904.09239
Autor:
Doerr, Benjamin, Krejca, Martin S.
Publikováno v:
In Theoretical Computer Science 6 January 2021 851:121-128
Estimation of Distribution Algorithms (EDAs) are stochastic heuristics that search for optimal solutions by learning and sampling from probabilistic models. Despite their popularity in real-world applications, there is little rigorous understanding o
Externí odkaz:
http://arxiv.org/abs/1807.10038
Publikováno v:
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2017). ACM, New York, NY, USA, 1383-1390
Unlike traditional evolutionary algorithms which produce offspring via genetic operators, Estimation of Distribution Algorithms (EDAs) sample solutions from probabilistic models which are learned from selected individuals. It is hoped that EDAs may i
Externí odkaz:
http://arxiv.org/abs/1802.00721
Autor:
Krejca, Martin S., Witt, Carsten
Publikováno v:
In Theoretical Computer Science 6 September 2020 832:143-165
Autor:
Doerr, Benjamin1 (AUTHOR) doerr@lix.polytechnique.fr, Krejca, Martin S.2 (AUTHOR) martin.krejca@lip.fr
Publikováno v:
Evolutionary Computation. Winter2021, Vol. 29 Issue 4, p543-563. 21p.
Autor:
Witt, Carsten
A runtime analysis of the Univariate Marginal Distribution Algorithm (UMDA) is presented on the OneMax function for wide ranges of its parameters $\mu$ and $\lambda$. If $\mu\ge c\log n$ for some constant $c>0$ and $\lambda=(1+\Theta(1))\mu$, a gener
Externí odkaz:
http://arxiv.org/abs/1704.00026