- author = "Justinian P. Rosca and Dana H. Ballard",
- title = "Hierarchical Self-Organization in Genetic Programming",
- booktitle = "Proceedings of the Eleventh International Conference on Machine Learning",
- year = "1994",
- pages = "251--258",
- address = "New Brunswick, NJ, USA",
- month = jul # " 10-13",
- publisher = "Morgan Kaufmann",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-1-55860-335-6",
-
URL = "
ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/94.ml.hierarchical_so_gp.ps.gz",
-
URL = "
https://www.sciencedirect.com/science/article/pii/B9781558603356500386",
-
DOI = "
doi:10.1016/B978-1-55860-335-6.50038-6",
- size = "8 pages",
- abstract = "This paper presents an approach to automatic discovery of functions in Genetic Programming. The approach is based on discovery of useful building blocks by analyzing the evolution trace, generalizing blocks to define new functions, and finally adapting the problem representation on-the-fly. Adapting the representation determines a hierarchical organization of the extended function set which enables a restructuring of the search space so that solutions can be found more easily. Measures of complexity of solution trees are defined for an adaptive representation framework. The minimum description length principle is applied to justify the feasibility of approaches based on a hierarchy of discovered functions and to suggest alternative ways of defining a problem's fitness function. Preliminary empirical results are presented.",
-
notes = "Also known as \cite{ROSCA1994251}
",
