Shrinking the Inductive Programming Search Space with Instruction Subsets

Edward McDaid, Sarah McDaid

2023

Abstract

Inductive programming frequently relies on some form of search in order to identify candidate solutions. However, the size of the search space limits the use of inductive programming to the production of relatively small programs. If we could somehow correctly predict the subset of instructions required for a given problem then inductive programming would be more tractable. We will show that this can be achieved in a high percentage of cases. This paper presents a novel model of programming language instruction co-occurrence that was built to support search space partitioning in the Zoea distributed inductive programming system. This consists of a collection of intersecting instruction subsets derived from a large sample of open source code. Using the approach different parts of the search space can be explored in parallel. The number of subsets required does not grow linearly with the quantity of code used to produce them and a manageable number of subsets is sufficient to cover a high percentage of unseen code. This approach also significantly reduces the overall size of the search space - often by many orders of magnitude.

Paper Citation

in Harvard Style

McDaid E. and McDaid S. (2023). Shrinking the Inductive Programming Search Space with Instruction Subsets. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 578-585. DOI: 10.5220/0011706900003393

in Bibtex Style

@conference{icaart23,
author={Edward McDaid and Sarah McDaid},
title={Shrinking the Inductive Programming Search Space with Instruction Subsets},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={578-585},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011706900003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Shrinking the Inductive Programming Search Space with Instruction Subsets
SN - 978-989-758-623-1
AU - McDaid E.
AU - McDaid S.
PY - 2023
SP - 578
EP - 585
DO - 10.5220/0011706900003393