The univariate marginal distribution algorithm copes well with deception and epistasis
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Publication:2057117
DOI10.1007/978-3-030-43680-3_4zbMath1484.68349arXiv2007.08277OpenAlexW3127499867MaRDI QIDQ2057117
Martin S. Krejca, Benjamin Doerr
Publication date: 8 December 2021
Full work available at URL: https://arxiv.org/abs/2007.08277
epistasisrun-time analysisunivariate marginal distribution algorithmestimation-of-distribution algorithm
Related Items (3)
A rigorous runtime analysis of the \((1 + (\lambda, \lambda))\) GA on jump functions ⋮ A simplified run time analysis of the univariate marginal distribution algorithm on LeadingOnes ⋮ The runtime of the compact genetic algorithm on jump functions
Cites Work
- Optimizing linear functions with the \((1 + \lambda)\) evolutionary algorithm -- different asymptotic runtimes for different instances
- A rigorous analysis of the compact genetic algorithm for linear functions
- On the analysis of the \((1+1)\) evolutionary algorithm
- Upper bounds on the running time of the univariate marginal distribution algorithm on OneMax
- On the choice of the update strength in estimation-of-distribution algorithms and ant colony optimization
- Analyzing randomized search heuristics via stochastic domination
- On the limitations of the univariate marginal distribution algorithm to deception and where bivariate EDAs might help
- Probability Inequalities for Sums of Bounded Random Variables
- Lower Bounds on the Run Time of the Univariate Marginal Distribution Algorithm on OneMax
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