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Abstract: Machine learning aims towards the acquisition of knowledge based on either experience from the interaction with the external environment or by analyzing the internal problem-solving traces. Both approaches can be implemented in the Genetic Programming (GP) paradigm. [Hillis, 1990] proves in an ingenious way how the first approach can work. There have not been any significant tests to prove that GP can take advantage of its own search traces. This paper presents an approach to automatic... (Update)
Context of citations to this paper: More
.... California at Berkeley, 515] University of Dortmund, 253, 257, 259, 372] University of Massachusetts, 477] University of Rochester, [155] total 28 reports in 16 institutes 4.5 Patents The following list contains the names of the patents of genetic programming. The list is...
...or less severe in problems of regression of Boolean functions. For 1 The notion of structure tree was introduced in [Rosca and Ballard, 1994a] with the goal of qualitatively analyzing program transformations during evolution. 69 example, the result of the Boolean...
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BibTeX entry: (Update)
Justinian P. Rosca and Dana H. Ballard, "Genetic Programming with Adaptive Representations," Technical Report 489, University of Rochester, Computer Science Department, 1994. http://citeseer.ist.psu.edu/rosca94genetic.html More
@techreport{ rosca94genetic,
author = "Justinian P. Rosca and Dana H. Ballard",
title = "Genetic Programming with Adaptive Representations",
number = "TR 489",
address = "Rochester, NY, USA",
year = "1994",
url = "citeseer.ist.psu.edu/rosca94genetic.html" }
Citations (may not include all citations):2138 Genetic Algorithms in Search (context) - Goldberg - 1989
1053 Genetic Programming: On the Programming of Computers by Mean.. (context) - Koza - 1992
660 An Introduction to Kolmogorov Complexity and its Application.. - Li, Vitanyi - 1993
392 A Theory and Methodology of Inductive Learning (context) - Michalski - 1983
185 Inferring Decision Trees Using the Minimum Description Lengt.. (context) - Quinlan, Rivest - 1989
128 How Learning Can Guide Evolution (context) - Hinton, Nowlan - 1987
86 Genetic Programming for Feature Discovery and Image Discrimi.. - Tackett - 1993
85 Genetic Programming II (context) - Koza - 1994
82 Competitive Environments Evolve Better Solutions for Complex.. - Angeline, Pollack - 1993
78 Genetic Programming and Emergent Intelligence - Angeline - 1994
77 Coevolving High Level Representations - Angeline, Pollack - 1994
54 A New Interpretation of Schema Notation that Overturns the B.. (context) - Antonisse - 1989
25 Learning with Genetic Algorithms: An Overview (context) - DeJong - 1988
20 An Introductory Analysis with Applications to Biology (context) - Holland, Natural et al. - 1992
16 Evolutionary Module Acquisition - Angeline, Pollack - 1993
15 Epistasis Variance - Suitability of a Representation to Gene.. (context) - Davidor - 1989
7 The Organization of Complex Systems (context) - Simon - 1973
6 Alternatives in Automatic Function Definition (context) - Kinnear - 1994
6 Beckenbach and Richard Bellman (context) - Edwin - 1965
1 Explicitely Schema Based Genetic Algorithms (context) - Deugo, Oppacher - 1990
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