BFO, a trainable derivative-free Brute Force Optimizer for nonlinear
   bound-constrained optimization and equilibrium computations with continuous
                           and discrete variables

                         M. Porcelli and Ph. L. Toint
                        naXys Technical Report 06-2015

Abstract. 
  A direct-search derivative-free Matlab optimizer for bound-constrained
  problems is described, whose remarkable features are its ability to handle a
  mix of continuous and discrete variables, a versatile interface as well as a
  novel self-training option. Its performance compares favourably with that of
  NOMAD, a state-of-the art package.  It is also applicable to multilevel
  equilibrium- or constrained-type problems.  Its easy-to-use interface
  provides a number of user-oriented features, such as checkpointing and
  restart, variable scaling and early termination tools.