The goal for this master’s thesis is to explore the application of evolutionary computation and artificial neural networks in the development of intelligent systems able to solve the problem of approximating the optimal strategy in a falling blocks game. While methods from these natural computation subfields have already been used for this problem, artificial neural networks have not achieved successful results until now, and symbiosis between these two approaches has not been tried yet. Three intelligent systems are proposed in this thesis. Each one of them computes, in a different way, the optimal strategy in a falling blocks game. The heuristic system uses a search-and-evaluation process, whose heuristic function is optimized through a real-coded genetic algorithm. The neural system employs instead artificial neural networks trained with supervised learning. Finally, the hybrid system combines the advantages of the two previous solutions.