Publication:
Genetic programming and deductive-inductive learning: a multistrategy approach

Loading...
Thumbnail Image
Identifiers
ISBN: 1-55860-556-8
Publication date
1998
Defense date
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
Morgan Kaufmann
Impact
Google Scholar
Export
Research Projects
Organizational Units
Journal Issue
Abstract
Genetic Programming (GP) is a machine learning technique that was not conceived to use domain knowledge for generating new candidate solutions. It has been shown that GP can benefit from domain knowledge obtained by other machine learning methods with more powerful heuristics. However, it is not obvious that a combination of GP and a knowledge intensive machine learning method can work better than the knowledge intensive method alone. In this paper we present a multi-strategy approach where an analytical and inductive approach (hamlet) and an evolutionary technique based on GP (EvoCK) are combined for the task of learning control rules for problem solving in planning. Results show that both methods complement each other, supplying to the other method what the other method lacks and obtaining better results than using each method alone.
Description
Proceedings of: 15th International Conference on Machine Learning, Madison (Wisconsin, USA), July 24-27, 1998.
Keywords
Bibliographic citation
Jude W. Shavlik (ed.), Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998). Mongan Kaufmann, 1998. P. 10-18. ISBN 1-55860-556-8