  A new global stochastic search, guided mainly through derivative-free
directional information computable from the sample statistical moments of the
design variables within a Monte Carlo setup, is proposed. The search is aided
by imparting to a directional update term, which parallels the conventional
Gateaux derivative used in a local search for the extrema of smooth cost
functionals, additional layers of random perturbations referred to as
'coalescence' and 'scrambling'. A selection scheme, constituting yet another
avenue for random perturbation, completes the global search. The
direction-driven nature of the search is manifest in the local extremization
and coalescence components, which are posed as martingale problems that yield
gain-like update terms upon discretization. As anticipated and numerically
demonstrated, to a limited extent, against the problem of parameter recovery
given the chaotic response histories of a couple of nonlinear oscillators, the
proposed method apparently provides for a more rational, more accurate and
faster alternative to most available evolutionary schemes, prominently the
particle swarm optimization.
