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European Conference on the Applications of Evolutionary Computation

EvoApplications 2016: Applications of Evolutionary Computation pp 3–18Cite as

Enhanced Multiobjective Population-Based Incremental Learning with Applications in Risk Treaty Optimization

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9597))

Abstract

The purpose of this paper is to revisit the Multiobjective Population-Based Incremental Learning method and show how its performance can be improved in the context of a real-world financial optimization problem. The proposed enhancements lead to both better performance and improvements in the quality of solutions. Its performance was assessed in terms of runtime and speedup when parallelized. Also, metrics such as the average number of solutions, the average hypervolume, and coverage have been used in order to compare the Pareto frontiers obtained by both the original and enhanced methods. Results indicated that the proposed method is 22.1 % faster, present more solutions in the average (better defining the Pareto frontier) and often generates solutions having larger hypervolumes. The enhanced method achieves a speedup of 15.7 on 16 cores of a dual socket Intel multi-core machine when solving a Reinsurance Contract Optimization problem involving 15 Layers or sub-contracts.

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Notes

  1. 1.

    In probability theory, the expected value, usually denoted by E[X], refers to the value of a random variable X that we would “expect” to find out if we could repeat the random variable process an infinite number of times and take the average of the values obtained.

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Acknowledgment

The authors would like to thank CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) and IFMA (Instituto Federal de Educação, Ciência e Tecnologia do Maranhão) for funding this research.

Author information

Authors and Affiliations

  1. Informatics Department, Instituto Federal Do Maranhão, São Luis, MA, Brazil

    Omar Andres Carmona Cortes

  2. Risk Analytics Lab, Dalhousie University, Halifax, NS, Canada

    Andrew Rau-Chaplin

Authors
  1. Omar Andres Carmona Cortes
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  2. Andrew Rau-Chaplin
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Corresponding author

Correspondence to Omar Andres Carmona Cortes .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

  2. Aalborg University, Copenhagen, Denmark

    Paolo Burelli

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Cortes, O.A.C., Rau-Chaplin, A. (2016). Enhanced Multiobjective Population-Based Incremental Learning with Applications in Risk Treaty Optimization. In: Squillero, G., Burelli, P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science(), vol 9597. Springer, Cham. https://doi.org/10.1007/978-3-319-31204-0_1

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  • DOI: https://doi.org/10.1007/978-3-319-31204-0_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31203-3

  • Online ISBN: 978-3-319-31204-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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