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This paper proposes a novel global optimization technique to solve the nonconvex economic load dispatch (NCELD) problem. The foraging strategy of the pachycondyla apicalis ant (API) is hybridized with a genetic algorithm (GA) strategy to incorporate key features of both API and GA and form a relatively simple but robust algorithm, entitled GAAPI. The novel algorithm proposed in this paper combines the downhill behavior of API (a key characteristic of optimization algorithms) and a good spreading in the solution space of the GA search strategy (a guarantee to avoid being trapped in local optima). The feasibility of the proposed method is tested for three different test systems having different size and complexity. The results are calculated in terms of solution quality and computational efficiency; it is shown that the proposed GAAPI is capable of obtaining highly robust, quality solutions in a reasonable computational time.