Genetic Programming and Deductive-Inductive Learning: A Multistrategy Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{icml98-ricardo,

• author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi",
• title = "Genetic Programming and Deductive-Inductive Learning: A Multistrategy Approach",
• booktitle = "Proceedings of the Fifteenth International Conference on Machine Learning, ICML'98",
• year = "1998",
• editor = "Jude Shavlik",
• pages = "10--18",
• address = "Madison, Wisconsin, USA",
• month = jul,
• publisher = "Morgan Kaufmann",
• keywords = "genetic algorithms, genetic programming, Learning in Planning, Multistrategy learning",
• ISBN = "1-55860-556-8",
• URL = "http://scalab.uc3m.es/~dborrajo/papers/icml98.ps.gz",
• size = "9 pages",
• 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 multistrategy approach where an already multistrategy approach ({\sc hamlet} combines analytical and inductive learning) 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.",
• notes = "ICML'98 http://www.cs.wisc.edu/icml98/ blocksworld many random problems generated in order to train system. No crossover, steady state population size = 2, tournament size = 2",

}

Genetic Programming entries for Ricardo Aler Mur Daniel Borrajo Pedro Isasi Vinuela

Citations