Learning by adapting representations in genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Rosca:1994:larGP,

• author = "J. P. Rosca and D. H. Ballard",
• title = "Learning by adapting representations in genetic programming",
• year = "1994",
• booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence",
• volume = "1",
• pages = "407--412",
• address = "Orlando, Florida, USA",
• month = "27-29 " # jun,
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming, evolution trace, external environment interaction, genetic search traces, internal problem-solving trace analysis, knowledge acquisition, knowledge representation adaptation, machine learning, search space restructuring, adaptive systems, knowledge acquisition, knowledge representation, learning (artificial intelligence), problem solving, programming, search problems",
• URL = "ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/94.ieee.adaptive_repr.ps.gz",
• DOI = "doi:10.1109/ICEC.1994.349916",
• size = "6 pages",
• abstract = "Machine learning aims towards the acquisition of knowledge based on either experience from the interaction with the external environment or by analysing the internal problem-solving traces. Genetic Programming (GP) has been effective in learning via interaction but so far there have not been any significant tests to show that GP can take advantage of its own search traces. This paper demonstrates how an analysis of the evolution trace enables the genetic search to discover useful genetic material and to use it in order to accelerate the search process. The key idea is that of genetic material discovery which enables a restructuring of the search space so that solutions can be much more easily found.",
• notes = "See \cite{Rosca94} for more details",

}

Genetic Programming entries for Justinian Rosca Dana H Ballard

Citations