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Parallel MO-PBIL: Computing Pareto Optimal Frontiers Efficiently with Applications in Reinsurance Analytics Leah Brown∗ , Anirudha Ashok Beria† , Omar A. C. Cortes‡ Andrew Rau-Chaplin∗ , Duane Wilson∗ , Neil Bu
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Document Date: 2014-05-23 12:07:00

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City

Improved Population / Parallel / Means Evaluation / London / /

Company

Zircon Computing LLC / John Wiley and Sons LTDA / GPU / Zircon Software / Intel / Willis Group / /

Country

United States / United Kingdom / /

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Facility

Rhodes University / Goethe University / Carnegie Mellon University / International Institute of Information Technology Gachibowli / Risk Analytics Lab Dalhousie University / /

IndustryTerm

evolutionary heuristic search methods / multi-objective search / enumurative search / heuristic search technique / insurance / search procedure / evolutionary search approach / standalone processor / evolutionary heuristic search method / computing / computational finance / evolutionary algorithms / search space / approximate solutions / /

Organization

International Institute of Information Technology Gachibowli / Goethe University / Frankfurt / Carnegie Mellon University / Dalhousie University / Rhodes University / /

Person

Omar A. C. Cortes‡ Andrew / MO-PBIL (Details) / Duane Wilson / Andrew Rau-Chaplin / Ashok Beria / Morgan Kauffman / MO-PBIL (Sketch) / Gallagher / running MO-PBIL / Neil Burke / /

Position

representative / model of reinsurance / scheduler / /

Product

OpenMP / Multi / /

ProgrammingLanguage

C++ / /

Technology

Multi-objective Optimization using Evolutionary Algorithms / standalone processor / Xeon processor / NTRODUCTION Parallel algorithms / Information Technology / Simulation / two Xeon processors / Xeon processors / sequential MOPBIL algorithm / /

URL

http /

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