The core of artificial intelligence and machine learning is to get computers to solve problems automatically. One of the great tools that attempt to achieve that goal is Genetic Programming (GP). As alternatives to GP, Scatter Programming (SP) is proposed in this paper. One of the main features of SP is to exploit local search in order to overcome some recently addressed drawbacks of GP, especially its highly disruption of its main operations; crossover and mutation. This work shows that SP has promising performance and results in solving machine learning problems.