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Add to EdgeTodd A. Anderson
Synthesizing Programs with Continuous Optimization
Nov 02, 2022
Automatic software generation based on some specification is known as program synthesis. Most existing approaches formulate program synthesis as a search problem with discrete parameters. In this paper, we present a novel formulation of program synthesis as a continuous optimization problem and use a state-of-the-art evolutionary approach, known as Covariance Matrix Adaptation Evolution Strategy to solve the problem. We then propose a mapping scheme to convert the continuous formulation into actual programs. We compare our system, called GENESYS, with several recent program synthesis techniques (in both discrete and continuous domains) and show that GENESYS synthesizes more programs within a fixed time budget than those existing schemes. For example, for programs of length 10, GENESYS synthesizes 28% more programs than those existing schemes within the same time budget.
Learning Fitness Functions for Genetic Algorithms
Sep 10, 2019
A genetic algorithm (GA) attempts to solve a problem using a pool of potential solutions that are iteratively refined using various selection techniques. Although GAs have been used successfully for many problems, one criticism is that hand-crafting a GA's fitness function, the test that aims to effectively guide its evolution, can be notably challenging. Moreover, the complexity of a GA's fitness function tends to grow proportionally with the complexity of the problem being solved. In this work, we present a novel approach to learn a GA's fitness function. For the purpose of simplicity, we limit the demonstration of this technique to automatic software program generation. However, our system has no specific restrictions that prevent it from being applied to other domains. We also augment the GA evolutionary process with a minimally intrusive search heuristic. This heuristic improves the GA's ability to discover correct programs from ones that are approximately correct and does so with negligible computational overhead. We compare our approach to two state-of-the-art program generation systems and demonstrate that it finds more correct programs with fewer candidate program generations.