Crossover Context in Page-based Linear Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{wilson:2003:ccpb,
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author = "G. C. Wilson and M. I. Heywood",
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title = "Crossover Context in Page-based Linear Genetic
Programming",
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booktitle = "IEEE CCECE 2002: IEEE Canadian Conference on
Electrical and Computer Engineering",
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year = "2002",
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editor = "W. Kinsner and A. Seback and K. Ferens",
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pages = "809--814",
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volume = "2",
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month = "12-15 " # may,
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organisation = "IEEE Canada",
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming, Strategy
Learning, learning (artificial intelligence), search
problems, San Mateo trail, artificial ants, code
sequences, crossover operator, effective search
strategies, fitness change, instructions, simple
register based memories, strategy learning",
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ISBN = "0-7803-7515-7",
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ISSN = "0840-7789",
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URL = "http://flame.cs.dal.ca/~gwilson/docs/papers/ccece_2002.pdf",
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DOI = "doi:10.1109/CCECE.2002.1013046",
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size = "6 pages",
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abstract = "This work explores strategy learning through genetic
programming in artificial ants that navigate the San
Mateo trail. We investigate several properties of
linearly structured (as opposed to typical tree based)
GP including: the significance of simple register based
memories, the significance of constraints applied to
the crossover operator, and how active the ant are. We
also provide a basis for investigating more thoroughly
the relation between specific code sequences and
fitness by dividing the genome into pages of
instructions and introducing an analysis of fitness
change and exploration of the trail done by particular
parts of a genome. By doing so we are able to present
results on how best to find the instructions in an
individual's program that contribute positively to the
accumulation of effective search strategies.",
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notes = "best student paper award",
- }
Genetic Programming entries for
Garnett Carl Wilson
Malcolm Heywood
Citations