Test Case Features as Hyper-heuristics for Inductive Programming

TL;DR

This paper introduces a novel hyper-heuristic approach using test case type signatures to select instruction subset families, significantly reducing the search space in inductive programming by up to three orders of magnitude.

Contribution

It proposes using test case type signatures as hyper-heuristics to dynamically select smaller instruction subset families, improving search efficiency in inductive programming.

Findings

Search space reduction of 1 to 3 orders of magnitude.

Smaller, more targeted subset families improve problem-solving efficiency.

Potential for further reductions with more sophisticated type systems.

Abstract

Instruction subsets are heuristics that can reduce the size of the inductive programming search space by tens of orders of magnitude. Comprising many overlapping subsets of different sizes, they serve as predictions of the instructions required to code a solution for any problem. Currently, this approach employs a single, large family of subsets meaning that some problems can search thousands of subsets before a solution is found. In this paper we introduce the use of test case type signatures as hyper-heuristics to select one of many, smaller families of instruction subsets. The type signature for any set of test cases maps directly to a single family and smaller families mean that fewer subsets need to be considered for most problems. Having many families also permits subsets to be reordered to better reflect their relative occurrence in human code - again reducing the search space size for many problems. Overall the new approach can further reduce the size of the inductive programming search space by between 1 and 3 orders of magnitude, depending on the type signature. Larger and more consistent reductions are possible through the use of more sophisticated type systems. The potential use of additional test case features as hyper-heuristics and some other possible future work is also briefly discussed.