Learning Fitness Functions for Machine Programming

The problem of automatic software generation has been referred to as machine programming. In this work, we propose a framework based on genetic algorithms to help make progress in this domain. Although genetic algorithms (GAs) have been successfully used for many problems, one criticism is that hand-crafting GAs fitness function, the test that aims to effectively guide its evolution, can be notably challenging. Our framework presents a novel approach to learn the fitness function using neural networks to predict values of ideal fitness functions.We also augment the evolutionary process with a minimally intrusive search heuristic. This heuristic improves the framework’s ability to discover correct programs from ones that are approximately correct and does so with negligible computational overhead. We compare our approach with several state-of-the-art program synthesis methods and demonstrate that it finds more correct programs with fewer candidate program generations.