3 citations · 7 across the 9 of their papers we have counts for
9 papers
Instruction and Solution Probabilities as Heuristics for Inductive Programming
Edward McDaid, Sarah McDaid
Instruction subsets (ISs) are heuristics that can shrink the size of the inductive programming (IP) search space by tens of orders of magnitude. Here, we extend the IS approach by…
Test Case Features as Hyper-heuristics for Inductive Programming
Edward McDaid, Sarah McDaid
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 differe…
Further Decimating the Inductive Programming Search Space with Instruction Digrams
Edward McDaid, Sarah McDaid
Overlapping instruction subsets derived from human originated code have previously been shown to dramatically shrink the inductive programming search space, often by many orders of…
Shrinking the Inductive Programming Search Space with Instruction Subsets
Edward McDaid, Sarah McDaid
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 programmi…
Architecture and Knowledge Representation for Composable Inductive Programming
Edward McDaid, Sarah McDaid
We present an update on the current architecture of the Zoea knowledge-based, Composable Inductive Programming system. The Zoea compiler is built using a modern variant of the blac…
The Composability of Intermediate Values in Composable Inductive Programming
Edward McDaid, Sarah McDaid
It is believed that mechanisms including intermediate values enable composable inductive programming (CIP) to be used to produce software of any size. We present the results of a s…