A variety of inductive program synthesis (IPS) techniques have recently been developed, emerging from di.erent areas of computer science. However, these techniques have not been adequately compared on general program synthesis problems. In this paper we compare several methods on problems requiring solution programs to handle various data types, control structures, and numbers of outputs. .e problem set also spans levels of abstraction; some would ordinarily be approached using machine code or assembly language, while others would ordinarily be approached using highlevel languages. .e presented comparisons are focused on the possibility of success; that is, on whether the system can produce a program that passes all tests, for all training and unseen testing inputs. .e compared systems are Flash Fill, MagicHaskeller, TerpreT, and two forms of genetic programming. .e two genetic programming methods chosen were PushGP and Grammar Guided Genetic Programming. .e results suggest that PushGP and, to an extent, TerpreT and Grammar Guided Genetic Programming are more capable of finding solutions than the others, albeit at a higher computational cost. A more salient observation is the dificulty of comparing these methods due to drastically di.erent intended applications, despite the common goal of program synthesis.
|Original language||English (US)|
|Title of host publication||GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||8|
|State||Published - Jul 15 2017|
|Event||2017 Genetic and Evolutionary Computation Conference Companion, GECCO 2017 - Berlin, Germany|
Duration: Jul 15 2017 → Jul 19 2017
|Name||GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion|
|Conference||2017 Genetic and Evolutionary Computation Conference Companion, GECCO 2017|
|Period||7/15/17 → 7/19/17|
Bibliographical noteFunding Information:
National Science Foundation under Grants No. 1617087, 1129139 and 1331283.
- Genetic programming
- Inductive Program Synthesis
- Machine Learning