Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/69485
Title: Effectively Tackling Reinsurance Problems by Using Evolutionary and Swarm Intelligence Algorithms
Author: Salcedo-Sanz, Sancho
Carro-Calvo, Leo
Claramunt Bielsa, M. Mercè
Castañer, Anna
Mármol, Maite
Keywords: Risc (Assegurances)
Assegurances de vida
Avaluació del risc
Algorismes
Risk (Insurance)
Life insurance
Risk assessment
Algorithms
Issue Date: 1-Apr-2014
Publisher: MDPI
Abstract: This paper is focused on solving different hard optimization problems that arise in the field of insurance and, more specifically, in reinsurance problems. In this area, the complexity of the models and assumptions considered in the definition of the reinsurance rules and conditions produces hard black-box optimization problems -problems in which the objective function does not have an algebraic expression, but it is the output of a system - usually a computer program, which must be solved in order to obtain the optimal output of the reinsurance. The application of traditional optimization approaches is not possible in this kind of mathematical problem, so new computational paradigms must be applied to solve these problems. In this paper, we show the performance of two evolutionary and swarm intelligence techniques -evolutionary programming and particle swarm optimization-. We provide an analysis in three black-box optimization problems in reinsurance, where the proposed approaches exhibit an excellent behavior, finding the optimal solution within a fraction of the computational cost used by inspection or enumeration methods.
Note: Reproducció del document publicat a: http://dx.doi.org/10.3390/risks2020132
It is part of: Risks , 2014, vol. 2014, num. 2, p. 132-145
Related resource: http://dx.doi.org/10.3390/risks2020132
URI: http://hdl.handle.net/2445/69485
ISSN: 2227-9091
Appears in Collections:Articles publicats en revistes (Matemàtica Econòmica, Financera i Actuarial)

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