In this paper we address the problem of program discovery as defined by Genetic Programming. We have two major results: First, by combining a hierarchical crossover operator with two traditional single point search algorithms: Simulated Annealing and Stochastic Iterated Hill Climbing, we have solved some problems with fewer fitness evaluations and a greater probability of a success than Genetic Programming. Second, we have managed to enhance Genetic Programming by hybridizing it with the simple scheme of hill climbing from a few individuals, at a fixed interval of generations. The new hill climbing component has two options for generating candidate solutions: mutation or crossover. When it uses crossover, mates are either randomly created, randomly drawn from the population at large, or drawn from a pool of fittest individuals.