  We propose a new stochastic algorithm (generalized simulated annealing) for
computationally finding the global minimum of a given (not necessarily convex)
energy/cost function defined in a continuous D-dimensional space. This
algorithm recovers, as particular cases, the so called classical ("Boltzmann
machine") and fast ("Cauchy machine") simulated annealings, and can be quicker
than both. Key-words: simulated annealing; nonconvex optimization; gradient
descent; generalized statistical mechanics.
