2026-04-14T02:57:10Zhttps://recercat.cat/oai/request

oai:recercat.cat:2445/694852025-12-05T05:55:31Zcom_2072_1057col_2072_478917col_2072_478919

00925njm 22002777a 4500 dc Salcedo-Sanz, Sancho author Carro-Calvo, Leo author Claramunt Bielsa, M. Mercè author Castañer, Anna author Mármol, Maite author 2016-02-17T10:14:30Z 2016-02-17T10:14:30Z 2014-04-01 2016-02-17T10:14:31Z 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. Risc (Assegurances) Assegurances de vida Avaluació del risc Algorismes Risk (Insurance) Life insurance Risk assessment Algorithms Effectively Tackling Reinsurance Problems by Using Evolutionary and Swarm Intelligence Algorithms