Genetic Algorithm for Program Synthesis
Yutaka Nagashima

TL;DR
This paper enhances a deductive program synthesis tool by integrating evolutionary computation to improve search efficiency, demonstrating generalization to new problems through cross-validation.
Contribution
It introduces a novel hybrid approach combining deductive synthesis with evolutionary algorithms to accelerate program derivation.
Findings
Improved search speed in program synthesis
Generalization of the method to unseen problems
Successful integration of evolutionary computation with deductive methods
Abstract
A deductive program synthesis tool takes a specification as input and derives a program that satisfies the specification. The drawback of this approach is that search spaces for such correct programs tend to be enormous, making it difficult to derive correct programs within a realistic timeout. To speed up such program derivation, we improve the search strategy of a deductive program synthesis tool, SuSLik, using evolutionary computation. Our cross-validation shows that the improvement brought by evolutionary computation generalises to unforeseen problems.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEvolutionary Algorithms and Applications · Teaching and Learning Programming · Software Engineering Research
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
