Skip to main content
SpringerLink
Log in

Enhanced multiobjective population-based incremental learning with applications in risk treaty optimization

  • Special Issue
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

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, which can represent millions of dollars for the insurance company in terms of recoveries. 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 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 .

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Notes

  1. Actually, premiums are stated by unit of limit, also know as a Rate on Line.

References

  1. Alba E (2002) Parallel evolutionary algorithms can achieve super-linear performance. Inf Process Lett 82(1):7–13

    Article  MathSciNet  MATH  Google Scholar 

  2. Baluja S (1994) Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning. Tech Rep, Pittsburgh

  3. Benfield AON (2014) Annual global climate and catastrophe report, impact forecasting. http://thoughtleadership.aonbenfield.com/Documents/20140113. Accessed 13 Apr 2014

  4. Brown L, Beria AA, Cortes O, Rau-Chaplin A, Wilson D, Burke N, Gaiser-Porter J (2014) Parallel MO-PBIL: computing pareto optimal frontiers efficiently with applications in reinsurance analytics. In: Conference on high performance computing simulation (HPCS), 2014 International, pp 766–775

  5. Bureerat S (2011) Improved population-based incremental learning in continuous spaces. Soft Comput Ind Appl 96:77–86

    Google Scholar 

  6. Coelho M, Rau-Chaplin A (2014) eXsight: an analytical framework for quantifying financial loss in the aftermath of catastrophic events. In: Proceedings of the workshop ISSASiM (DEXA 2014)

  7. Cortes O, Rau-Chaplin A, Wilson D, Gaiser-Porter J (2014) On PBIL, DE and PSO for optimization of reinsurance contracts. In: Esparcia-Alczar AI, Mora AM (eds) Applications of evolutionary computation, lecture notes in computer science. Springer, Berlin, pp 227–238

  8. Cortes OAC, Rau-Chaplin A, Wilson D, Cook I, Gaiser-Porter J (2013) Efficient optimization of reinsurance contracts using discretized PBIL. In: The third international conference on data analytics, pp 18–24

  9. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks, vol 4. IEEE, pp 1942–1948

  10. Michalewicz Z (1999) Genetic algorithms + Data structure = Evolution programs, 3 edn

  11. Mistry S, Gaiser-Porter J, McSharry P, Armour T (2013) Parallel computation of reinsurance models (Unpublished)

  12. Mitschele A, Oesterreicher I, Schlottmann F, Seese D (2015) Heuristic optimization of reinsurance programs and implications for reinsurance buyers. In: Operations research proceedings, pp 287–292

  13. Montgomery D, Runger GC (2010) Applied statistics and probability forengineers. Wiley, Hoboken

    Google Scholar 

  14. Oesterreicher I, Mitschele A, Schlottmann F, Seese D (2006) Comparison of multi-objective evolutionary algorithms in optimizing combinations of reinsurance contracts. In: Proceedings of the 8th annual conference on genetic and evolutionary computation, GECCO ’06ACM, New York, NY, pp 747–748

  15. Salcedo-Sanz S, Carro-Calvo L, Claramunt M, Castaer A, Mrmol M (2014) Effectively tackling reinsurance problems by using evolutionary and swarm intelligence algorithms. Risks 2(2):132

    Article  Google Scholar 

  16. Servais M, de Jager G, Greene JR (1997) Function optimisation using multiple-base population based incremental learning. In: The eighth annual South African workshop on pattern recognition, Rhodes University

  17. Storn R, Price K (1995) Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces. ftp://ftp.icsi.berkeley.edu/pub/techreports/1995/tr-95-012.pdf

  18. Tierney L, Rossini AJ, Li N, Sevcikova H Snow package. https://cran.r-project.org/web/packages/snow/

  19. Wang H, Cortes O, Rau-Chaplin A (2015) Dynamic optimization of multi-layered reinsurance treaties. In: The 30th ACM/SIGAPP symposium on applied computing

  20. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evolut Comput 3(2):82–102

    Article  Google Scholar 

  21. Yuan B, Gallagher M (2003) Playing in continuous spaces: some analysis and extension of population-based incremental learning. In: IEEE Congress on evolutionary computation. IEEE, pp 443–450

  22. Zhang QH (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evolut Comput 11(6):712–731

    Article  Google Scholar 

Download references

Acknowledgements

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. We also would like to thank Willies group for providing the anonymous data.

Author information

Authors and Affiliations

  1. Informatics Department, Instituto Federal do Maranhão, São Luís, MA, Brazil

    Omar Andres Carmona Cortes

  2. Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada

    Andrew Rau-Chaplin

Authors
  1. Omar Andres Carmona Cortes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrew Rau-Chaplin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Omar Andres Carmona Cortes.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Carmona Cortes, O.A., Rau-Chaplin, A. Enhanced multiobjective population-based incremental learning with applications in risk treaty optimization . Evol. Intel. 9, 153–165 (2016). https://doi.org/10.1007/s12065-016-0147-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-016-0147-0

Keywords

Search

Navigation