  A global optimization framework, acronymed COMBEO (Change OfMeasure Based
Evolutionary Optimization), is proposed. An important aspect in the development
is a set of derivative-free additive directional terms obtainable through a
change of measures en route to the imposition of any stipulated conditions
aimed at driving the realized design variables (particles) to the global
optimum. The generalized setting offered by the new approach also enables
several basic ideas, used with other global search methods such as the particle
swarm or the differential evolution, to be rationally incorporated in the
proposed setup via a change of measures. The global search may be further aided
by imparting to the directional update terms additional layers of random
perturbations such as scrambling and selection. Depending on the precise choice
of the optimality conditions and the extent of random perturbation, the search
can be readily rendered either greedy or more exploratory. As numerically
demonstrated, the new proposal appears to provide for a more rational, more
accurate and faster alternative to most available evolutionary optimization
schemes, prominent amongst which are the differential evolution and the
particle swarm methods.
