# Synthesizing Programs with Continuous Optimization

arXiv: https://arxiv.org/abs/2211.00828 · HTML: https://mlsanity.com/paper/2211.00828/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 61 references that are in the map.
- First posted on arXiv 2 Nov 2022.

## Builds on (most cited first, 24 of 24)

- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [The CMA Evolution Strategy: A Tutorial](https://mlsanity.com/paper/1604.00772/) (2016)
- [Hierarchical Representations for Efficient Architecture Search](https://mlsanity.com/paper/1711.00436/) (2017)
- [Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning](https://mlsanity.com/paper/1712.06567/) (2017)
- [DeepCoder: Learning to Write Programs](https://mlsanity.com/paper/1611.01989/) (2016)
- [AutoML-Zero: Evolving Machine Learning Algorithms From Scratch](https://mlsanity.com/paper/2003.03384/) (2020)
- [RobustFill: Neural Program Learning under Noisy I/O](https://mlsanity.com/paper/1703.07469/) (2017)
- [Neural Programmer-Interpreters](https://mlsanity.com/paper/1511.06279/) (2015)
- [Neuro-Symbolic Program Synthesis](https://mlsanity.com/paper/1611.01855/) (2016)
- [Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis](https://mlsanity.com/paper/1805.04276/) (2018)
- [Learning to Infer Program Sketches](https://mlsanity.com/paper/1902.06349/) (2019)
- [Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples](https://mlsanity.com/paper/1804.01186/) (2018)
- [Making Neural Programming Architectures Generalize via Recursion](https://mlsanity.com/paper/1704.06611/) (2017)
- [Latent Execution for Neural Program Synthesis](https://mlsanity.com/paper/2107.00101/) (2021)
- [Program Synthesis using Conflict-Driven Learning](https://mlsanity.com/paper/1711.08029/) (2017)
- [Automatic Program Synthesis of Long Programs with a Learned Garbage Collector](https://mlsanity.com/paper/1809.04682/) (2018)
- [Synthesizing Datalog Programs Using Numerical Relaxation](https://mlsanity.com/paper/1906.00163/) (2019)
- [Latent Programmer: Discrete Latent Codes for Program Synthesis](https://mlsanity.com/paper/2012.00377/) (2020)
- [Optimal Neural Program Synthesis from Multimodal Specifications](https://mlsanity.com/paper/2010.01678/) (2020)
- [The Three Pillars of Machine Programming](https://mlsanity.com/paper/1803.07244/) (2018)
- [Learning Fitness Functions for Machine Programming](https://mlsanity.com/paper/1908.08783/) (2019)
- [AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms](https://mlsanity.com/paper/1709.05703/) (2017)
- [Relational Program Synthesis](https://mlsanity.com/paper/1809.02283/) (2018)

## Built on by (most cited first, 2 of 2)

- [Large Language Models Based Automatic Synthesis of Software Specifications](https://mlsanity.com/paper/2304.09181/) (2023)
- [Learning-Based Automatic Synthesis of Software Code and Configuration](https://mlsanity.com/paper/2305.15642/) (2023)

## Abstract (arXiv)

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.