An Analysis of black-box optimization problems in reinsurance : evolutionary-based approaches

  1. Salcedo-Sanz, Sancho 1
  2. Carro Calvo, L. 1
  3. Claramunt Bielsa, M. Mercè 2
  4. Castañer, Anna 2
  5. Mármol, Maite 2
  1. 1 Universidad de Alcalá
    info
    Universidad de Alcalá

    Alcalá de Henares, España

    ROR https://ror.org/04pmn0e78

    Geographic location of the organization Universidad de Alcalá
  2. 2 Universitat de Barcelona
    info
    Universitat de Barcelona

    Barcelona, España

    ROR https://ror.org/021018s57

    Geographic location of the organization Universitat de Barcelona
Journal:
Documentos de trabajo ( XREAP )

Year of publication: 2013

Issue: 4

Type: Working paper

DIALNET GOOGLE SCHOLAR lock_openDDD editor HANDLE: https://hdl.handle.net/2072/253817

Sustainable development goals

Abstract

Black-box optimization problems (BBOP) are de ned as those optimization problems in which the objective function does not have an algebraic expression, but it is the output of a system (usually a computer program). This paper is focussed on BBOPs that arise in the eld of insurance, and more speci cally in reinsurance problems. In this area, the complexity of the models and assumptions considered to de ne the reinsurance rules and conditions produces hard black-box optimization problems, that must be solved in order to obtain the optimal output of the reinsurance. The application of traditional optimization approaches is not possible in BBOP, so new computational paradigms must be applied to solve these problems. In this paper we show the performance of two evolutionary-based techniques (Evolutionary Programming and Particle Swarm Optimization). We provide an analysis in three BBOP in reinsurance, where the evolutionary-based approaches exhibit an excellent behaviour, nding the optimal solution within a fraction of the computational cost used by inspection or enumeration methods.

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