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pro vyhledávání: '"Nguyen, Phan Trung Hai"'
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
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 15th International Conference on Parallel Problem Solving from Nature 2018 (PPSN XV)
The Population-Based Incremental Learning (PBIL) algorithm uses a convex combination of the current model and the empirical model to construct the next model, which is then sampled to generate offspring. The Univariate Marginal Distribution Algorithm
Externí odkaz:
http://arxiv.org/abs/1806.01710
Autor:
Nguyen, Phan Trung Hai, Sudholt, Dirk
Publikováno v:
Proceedings of the 2018 Genetic and Evolutionary Computation Conference
Memetic algorithms are popular hybrid search heuristics that integrate local search into the search process of an evolutionary algorithm in order to combine the advantages of rapid exploitation and global optimisation. However, these algorithms are n
Externí odkaz:
http://arxiv.org/abs/1804.06173
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:
Nguyen, Phan Trung Hai, Sudholt, Dirk
Publikováno v:
In Artificial Intelligence October 2020 287
Akademický článek
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Publikováno v:
Algorithmica; Feb2019, Vol. 81 Issue 2, p668-702, 35p