Here we present a new hybrid genetic algorithm (HGA) with the Baldwin effect. In the HGA, a local search is employed to change the fitness of individuals but the acquired improvements do not change the individual itself. This local search step exploits the Baldwin effect. Some numerical applications show that this algorithm can yield the global optimum more efficiently than commonly used HGAs. A theorem is presented that guarantees the convergence in probability of the new HGA.