Abstract
Automatic programming, the task of generating computer programs compliant with a specification without a human developer, is usually tackled either via genetic programming methods based on mutation and recombination of programs, or via neural language models. We propose a novel method that combines both approaches using a concept of a virtual neuro-genetic programmer, or scrum team. We demonstrate its ability to provide performant and explainable solutions for various OpenAI Gym tasks, as well as inject expert knowledge into the otherwise data-driven search for solutions.
Original language | English |
---|---|
Title of host publication | GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion |
Publisher | Association for Computing Machinery, Inc |
Pages | 329-330 |
Number of pages | 2 |
ISBN (Electronic) | 9781450383516 |
DOIs | |
Publication status | Published - 7 Jul 2021 |
Event | 2021 Genetic and Evolutionary Computation Conference, GECCO 2021 - Virtual/Online, Lille, France Duration: 10 Jul 2021 → 14 Jul 2021 https://gecco-2021.sigevo.org/HomePage |
Conference
Conference | 2021 Genetic and Evolutionary Computation Conference, GECCO 2021 |
---|---|
Abbreviated title | GECCO 2021 |
Country/Territory | France |
City | Lille |
Period | 10/07/21 → 14/07/21 |
Internet address |
Bibliographical note
Funding Information:This work was funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement 812882. This work is part of "Personal Health Interfaces Leveraging HUman-MAchine Natural interactionS" (PhilHumans) project
Publisher Copyright:
© 2021 Owner/Author.
Keywords
- genetic programming
- program synthesis
- reinforcement learning