38 citations · 109 across the 9 of their papers we have counts for
6 papers · 1 filter
On the Limitations of the Univariate Marginal Distribution Algorithm to Deception and Where Bivariate EDAs might help
Per Kristian Lehre, Phan Trung Hai Nguyen
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…
Runtime Analysis of the Univariate Marginal Distribution Algorithm under Low Selective Pressure and Prior Noise
Per Kristian Lehre, Phan Trung Hai Nguyen
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 ev…
Level-Based Analysis of the Univariate Marginal Distribution Algorithm
Duc-Cuong Dang, Per Kristian Lehre, Phan Trung Hai Nguyen
Estimation of Distribution Algorithms (EDAs) are stochastic heuristics that search for optimal solutions by learning and sampling from probabilistic models. Despite their popularit…
Level-Based Analysis of the Population-Based Incremental Learning Algorithm
Per Kristian Lehre, Phan Trung Hai Nguyen
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…
Memetic Algorithms Beat Evolutionary Algorithms on the Class of Hurdle Problems
Phan Trung Hai Nguyen, Dirk Sudholt
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…
Improved Runtime Bounds for the Univariate Marginal Distribution Algorithm via Anti-Concentration
Per Kristian Lehre, Phan Trung Hai Nguyen
Unlike traditional evolutionary algorithms which produce offspring via genetic operators, Estimation of Distribution Algorithms (EDAs) sample solutions from probabilistic models wh…