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PBILPopulation-Based Incremental Learning (theoretical statistics)
PBILPôle Bio-Informatique Lyonnais
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References in periodicals archive ?
In order to compare the quality of our proposal with the participants of OAEI 2017 (http://oaei.ontologymatching.org/ 2017/results/index.html) and Population-Based Incremental Learning Algorithm (PBIL) [20], which is a state-of-the-art compact EA-based ontology matching technique, we evaluate the obtained alignments with traditional recall, precision, and f-measure.
Performance assessment is made by comparing the proposed optimiser with GA, UMDA, BPSO, BSA, and PBIL by using the CEC2015 test problems.
In Section 2, we give a background overview of PBIL, EAG, other closely related EDA approaches, and large-scale global optimization problems.
Finally, notice that M-BOA works with variable vectors, which is much preferable for real variable problems or components, instead of probability vector as PBIL and cGA.
CRISIL's rating on the bank facilities of Plastiblends India Ltd (PBIL) continues to reflect PBIL's established market in the masterbatch business, and robust financial risk profile, marked by low gearing and strong debt protection metrics.
of British Columbia, Vancouver, British Columbia, Canada (pbil.evans@ubc.ca, icullis@hotmail.com); and Group Leader (Durability and Protection), FPInnovations, Vancouver, British Columbia, Canada (paul@van.forintek.ca).
Among the rising indicators is the NAPL Printing Business Index (PBIL the association's broadest measure of print activity, which rose to 59.9 in April from 58.1 in March and 54.0 in January.
Working with Glaxo Wellcome, the Imperial Cancer Research Fund (ICRF), The Swiss Institute of Bioinformatics (SIB), and the Lyons Bioinformatics Center (PBIL) at the Univ.
Those algorithms include the artificial bee colony optimization (ABC), adaptive differential evolution with optional external archive (JADE), population-based incremental learning (PBIL), teaching-learning-based optimization (TLBO), the real-code ant colony optimization (ACOR), a grey wolf optimizer (GWO), a Jaya algorithm (Jaya), and a sine cosine algorithm (SCA).
Population-based incremental learning (PBIL) is chosen and adapted to deal with truss antioptimisation leading to five variants of PBIL.