ABSTRACT: We propose an adaptive resolution approach to genetic algorithm (arGA) for solving Non convex Mixed Integer Non-Linear Programming (MINLP) problems. The technique uses the entropy measure of each variable to adjust the intensity of the genetic search around promising individuals. Performance is further improved by hybridization with adaptive resolution based local search (arLS) operator. In this poster, we describe the challenges and design choices involved in parallelization of this algorithm to solve complex MINLPs over nVidia Fermi GPU using CUDA. Results section shows several numerical tests and performance measurements obtained by running the algorithm over an nVidia Fermi GPU. We show that for difficult problems we can obtain a speedup of up to 15x with double precision and up to 34x with single precision.