Level-based analysis of the population-based incremental learning algorithm
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Authors
Colleges, School and Institutes
Abstract
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 (UMDA) is a special case of the PBIL, where the current model is ignored. Dang and Lehre (GECCO 2015) showed that UMDA can optimise LEADINGONES efficiently. The question still remained open if the PBIL performs equally well. Here, by applying the level-based theorem in addition to Dvoretzky-Kiefer-Wolfowitz inequality, we show that the PBIL optimises LEADINGONES in expected time O (nλ log λ + n2) for a population size λ = Ω(log n), which matches the bound of the UMDA. Finally, we showthat the result carries over to BINVAL giving the fist runtime result for the PBIL on the BINVAL problem.the bound of the UMDA. Finally,we show that the result carries over to BinVal
Details
Original language | English |
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Title of host publication | Proceedings of the 15th International Conference on Parallel Problem Solving from Nature 2018 (PPSN XV) |
Publication status | Published - 5 Oct 2018 |
Event | 15th International Conference on Parallel Problem Solving from Nature 2018 (PPSN XV) - Coimbra, Portugal Duration: 8 Sep 2018 → 12 Sep 2018 |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 15th International Conference on Parallel Problem Solving from Nature 2018 (PPSN XV) |
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Country | Portugal |
City | Coimbra |
Period | 8/09/18 → 12/09/18 |
Keywords
- population-based incremental learning, LeadingOnes, BinVal, running time analysis, level-based analysis, theory