# HOTGP -- Higher-Order Typed Genetic Programming

arXiv: https://arxiv.org/abs/2304.03200 · HTML: https://mlsanity.com/paper/2304.03200/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 6 Apr 2023.

## Builds on (most cited first, 3 of 3)

- [Problem-solving benefits of down-sampled lexicase selection](https://mlsanity.com/paper/2106.06085/) (2021)
- [Code Building Genetic Programming](https://mlsanity.com/paper/2008.03649/) (2020)
- [Functional Code Building Genetic Programming](https://mlsanity.com/paper/2206.04561/) (2022)

## Abstract (arXiv)

Program synthesis is the process of generating a computer program following a set of specifications, which can be a high-level description of the problem and/or a set of input-output examples. The synthesis can be modeled as a search problem in which the search space is the set of all the programs valid under a grammar. As the search space is vast, brute force is usually not viable and search heuristics, such as genetic programming, also have difficulty navigating it without any guidance. In this paper we present HOTGP, a new genetic programming algorithm that synthesizes pure, typed, and functional programs. HOTGP leverages the knowledge provided by the rich data-types associated with the specification and the built-in grammar to constrain the search space and improve the performance of the synthesis. The grammar is based on Haskell's standard base library (the synthesized code can be directly compiled using any standard Haskell compiler) and includes support for higher-order functions, λ-functions, and parametric polymorphism. Experimental results show that, when compared to 6 state-of-the-art algorithms using a standard set of benchmarks, HOTGP is competitive and capable of synthesizing the correct programs more frequently than any other of the evaluated algorithms.