- author = "Michael O'Neill and Lee Spector",
- title = "Automatic programming: The open issue?",
- journal = "Genetic Programming and Evolvable Machines",
- year = "2020",
- volume = "21",
- number = "1-2",
- pages = "251--262",
- month = jun,
- note = "Twentieth Anniversary Issue",
- keywords = "genetic algorithms, genetic programming, automatic programming, open issue",
- ISSN = "1389-2576",
-
DOI = "
doi:10.1007/s10710-019-09364-2",
- size = "12 pages",
- abstract = "Automatic programming, the automatic generation of a computer program given a high-level statement of the programs desired behaviour, is a stated objective of the field of genetic programming. As the general solution to a computational problem is to write a computer program, and given that genetic programming can automatically generate a computer program, researchers in the field of genetic programming refer to its ability to automatically solve problems. Genetic programming has also been described as an invention machine that is capable of generating human-competitive solutions. We argue that the majority of success and focus of our field has not actually been as a result of automatic programming. We set out to challenge the genetic programming community to refocus our research towards the objective of automatic programming, and to do so in a manner that embraces a wider perspective encompassing the related fields of, for example, artificial intelligence, machine learning, analytics, optimisation and software engineering.",
-
notes = "AI-complete problem. artificial general intelligence.
code synthesis.
'GP has the best track record'",
