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A versatile multi-objective FLUKA optimization using Genetic Algorithms

Quite often Monte Carlo simulation studies require a multi phase-space optimization, a complicated task, heavily relying on the operator experience and judgment. Examples of such calculations are shielding calculations with stringent conditions in the cost, in residual dose, material properties and...

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Detalles Bibliográficos
Autores principales: Vlachoudis, Vasilis, Antoniucci, Guido Arnau, Mathot, Serge, Kozlowska, Wioletta Sandra, Vretenar, Maurizio
Lenguaje:eng
Publicado: 2017
Materias:
Acceso en línea:https://dx.doi.org/10.1051/epjconf/201715306004
http://cds.cern.ch/record/2314948
Descripción
Sumario:Quite often Monte Carlo simulation studies require a multi phase-space optimization, a complicated task, heavily relying on the operator experience and judgment. Examples of such calculations are shielding calculations with stringent conditions in the cost, in residual dose, material properties and space available, or in the medical field optimizing the dose delivered to a patient under a hadron treatment. The present paper describes our implementation inside flair[1] the advanced user interface of FLUKA[2,3] of a multi-objective Genetic Algorithm[Erreur ! Source du renvoi introuvable.] to facilitate the search for the optimum solution.