{"type":"ScholarlyArticle","title":"Hierarchical Genetic Algorithms Operating on Populations of Computer Programs","authors":[{"name":"John R. Koza","slug":"john-r-koza","given":"John R.","family":"Koza","affiliation":""}],"abstract":"Existing approaches to artificial intelligence problems such as sequence induction, automatic programming, machine learning, planning, and pattern recognition typically require specification in advance of the size and shape of the solution to the problem (often in a unnatural and difficult way). This paper reports on a new approach in which the size and shape of the solution to such problems is dynamically created using Darwinian principles of reproduction and survival of the fittest. Moreover, the resulting solution is inherently hierarchical. The paper describes computer experiments, using the author's 4341 line LISP program, in five areas of artifical intelligence, namely (1) sequence induction, (2) automatic programming, (3) machine learning of functions, (4) planning, and (5) pattern recognition.","doi":null,"url":"https://ijcai.org/proceedings/1989/123","pdf":"https://ijcai.org/proceedings/1989/0123.pdf","markdown":"https://ijcai.org/proceedings/1989/123.md","bibtex":"https://ijcai.org/proceedings/1989/bibtex/123","volume":{"slug":"1989","title":"Proceedings of the Eleventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1989","url":"https://ijcai.org/proceedings/1989/","year":1989,"location":"Detroit, Michigan, USA","dates":"August 20-25 1989","editors":"N. S. Sridharan","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Machine Learning","track":"Machine Learning","number":123,"pages":{"first":768,"last":773},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}