Improving the Scalability of Generative Representations
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- @InCollection{Hornby:2007:GPTP,
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author = "Gregory S. Hornby",
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title = "Improving the Scalability of Generative
Representations",
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booktitle = "Genetic Programming Theory and Practice {V}",
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year = "2007",
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editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
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series = "Genetic and Evolutionary Computation",
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chapter = "8",
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pages = "127--144",
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address = "Ann Arbor",
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month = "17-19" # may,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-0-387-76308-8",
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DOI = "doi:10.1007/978-0-387-76308-8_8",
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size = "17 pages",
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abstract = "With the recent examples of the human-competitiveness
of evolutionary design systems, it is not of interest
to scale them up to produce more sophisticated designs.
Here we argue that for computer-automated design
systems to scale to producing more sophisticated
results they must be able to produce designs with
greater structure and organisation. By structure and
organization we mean the characteristics of modularity,
reuse and hierarchy (MR&H), characteristics that
are found both in man-made and natural designs. We
claim that these characteristics are enabled by
implementing the attributes of combination,
control-flow and abstraction in the representation, and
define metrics for measuring MR&H and define two
measures of overall structure and organisation by
combining the measures of MR&H. To demonstrate the
merit of our complexity measures, we use an
evolutionary algorithm to evolve solutions to different
sizes for a table design problem, and compare the
structure and organisation scores of the best tables
against existing complexity measures. We find that our
measures better correlate with the complexity of good
designs than do others, which supports our claim that
MR&H are important components of complexity. We
also compare evolution using five representations with
different combinations of MR&H, and find that the
best designs are achieved when all three of these
attributes are present. The results of this second set
of experiments demonstrate that implementing
representations with MR&H can greatly improve
search performance.",
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notes = "part of \cite{Riolo:2007:GPTP} Published 2008",
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affiliation = "NASA Ames Research Center U. C. Santa Cruz, Mail Stop
269-3 Moffett Field CA 94035",
- }
Genetic Programming entries for
Gregory S Hornby
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