Investigating a Parallel Breeder Genetic Algorithm on the inverse Aerodynamic design

Breeder Genetic Algorithms represent a class of random optimisation techniques gleaned from the science of population genetics, which have proved their ability to solve hard optimisation problems with continuous parameters. In this paper we test a parallel version of this technique against a sequential Breeder Genetic Algorithm on a typical inverse design problem in Aerodynamics, the problem of an aerofoil geometry recover starting from a target pressure distribution. Our results show that Parallel Breeder Genetic Algorithms are well suited for applications in Aerodynamics.