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A new global stochastic search, guided mainly through derivative-freedirectional information computable from the sample statistical moments of thedesign variables within a Monte Carlo setup, is proposed. The search is aidedby imparting to a directional update term, which parallels the conventionalGateaux derivative used in a local search for the extrema of smooth costfunctionals, additional layers of random perturbations referred to as'coalescence' and 'scrambling'. A selection scheme, constituting yet anotheravenue for random perturbation, completes the global search. Thedirection-driven nature of the search is manifest in the local extremizationand coalescence components, which are posed as martingale problems that yieldgain-like update terms upon discretization. As anticipated and numericallydemonstrated, to a limited extent, against the problem of parameter recoverygiven the chaotic response histories of a couple of nonlinear oscillators, theproposed method apparently provides for a more rational, more accurate andfaster alternative to most available evolutionary schemes, prominently theparticle swarm optimization.

Evolutionary global optimization posed as a randomly perturbed martingale problem and applied to parameter recovery of chaotic oscillators