- author = "S. M. Winkler and M. Affenzeller and S. Wagner",
- title = "Advances in applying genetic programming to machine learning, focussing on classification problems",
- booktitle = "20th International Parallel and Distributed Processing Symposium, IPDPS 2006",
- year = "2006",
- pages = "8 pp.?",
- address = "Rhodes Island",
- month = "25-29 " # apr # " 2006",
- publisher = "IEEE",
- keywords = "genetic algorithms, genetic programming",
- ISBN = "1-4244-0054-6",
-
DOI = "
doi:10.1109/IPDPS.2006.1639524",
- abstract = "A genetic programming based approach for solving classification problems is presented in this paper. Classification is understood as the act of placing an object into a set of categories, based on the object's properties; classification algorithms are designed to learn a function which maps a vector of object features into one of several classes. This is done by analysing a set of input-output examples (training samples) of the function. Here we present a method based on the theory of genetic algorithms and genetic programming that interprets classification problems as optimisation problems: Each presented instance of the classification problem is interpreted as an instance of an optimization problem, and a solution is found by a heuristic optimisation algorithm. The major new aspects presented in this paper are suitable genetic operators for this problem class (mainly the creation of new hypotheses by merging already existing ones and their detailed evaluation) we have designed and implemented. The experimental part of the paper documents the results produced using new hybrid variants of genetic algorithms as well as investigated parameter settings.",
-
notes = "CD-ROM
Dept. of Software Eng., Upper Austrian Univ. of Appl. Sci., Hagenberg, Austria",
