Genetic Algorithm for Program Synthesis
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 20 references that are in the map. First posted on arXiv 22 Nov 2022.
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12 of 20 references.
References · 12
- 2019Learning to Prove Theorems via Interacting with Proof Assistantscited by 107
- 2018Reinforcement Learning of Theorem Provingcited by 69
- 2018GamePad: A Learning Environment for Theorem Provingcited by 48
- 2017HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Provingcited by 26
- 2018PaMpeR: Proof Method Recommendation System for Isabelle/HOLcited by 7
- 2016A Proof Strategy Language and Proof Script Generation for Isabelle/HOLcited by 5
- 2019Towards Evolutionary Theorem Proving for Isabelle/HOLcited by 4
- 2018Structuring the Synthesis of Heap-Manipulating Programs - Extended Versioncited by 3
- 2019LiFtEr: Language to Encode Induction Heuristics for Isabelle/HOLcited by 3
- 2020Simple Dataset for Proof Method Recommendation in Isabelle/HOL (Dataset Description)cited by 3
- 2020Faster Smarter Induction in Isabelle/HOLcited by 2
- 2020Definitional Quantifiers Realise Semantic Reasoning for Proof by Inductioncited by 1
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