Automated Extraction of Expert Domain Knowledge from Genetic Programming Synthesis Results
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InCollection{Mcconaghy:2008:GPTP,
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author = "Trent McConaghy and Pieter Palmers and
Georges Gielen and Michiel Steyaert",
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title = "Automated Extraction of Expert Domain Knowledge from
Genetic Programming Synthesis Results",
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booktitle = "Genetic Programming Theory and Practice {VI}",
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year = "2008",
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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 = "111--125",
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address = "Ann Arbor",
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month = "15-17 " # may,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, synthesis,
domain knowledge, multi-objective, data mining, analog,
integrated circuits, age layered population structure",
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DOI = "doi:10.1007/978-0-387-87623-8_8",
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URL = "http://trent.st/content/2008-GPTP-synthesis_insight.pdf",
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size = "14 pages",
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isbn13 = "978-0-387-87622-1",
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abstract = "Recent work in genetic programming shows how expert
domain knowledge can be input to a genetic programming
(GP) synthesis system, to speed it up by orders of
magnitude and give trustworthy results. On the flip
side, this paper shows how expert domain knowledge can
be output from the results of a synthesis run, in forms
that are immediately recognisable and transferable for
problem domain experts. Specifically, using the
application of analog circuit design, this paper
presents a methodology to automatically generate a
decision tree for navigating from performance
specifications to topology choice; a means to extract
the relative importances of topology and parameters on
performance; and to generate whitebox models that
capture tradeoffs among performances. The extraction
uses a combination of data-mining and genetic
programming technologies. This paper also presents
techniques to ensure that the GP-based synthesis system
can indeed create a richly-populated, high-performance
dataset, including: a parallel-computing,
multi-objective age-layered population structure (ALPS)
for fast and reliable convergence; average ranking on
Pareto fronts (ARF) to handle many objectives; and
generating good initial topology sizings via multigate
constraint satisfaction. Results are shown on
operational amplifier synthesis across thousands of
topologies that generated a database containing
thousands of Pareto-optimal designs across five
objectives and dozens of constraints.",
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notes = "part of \cite{Riolo:2008:GPTP} published in 2009",
- }
Genetic Programming entries for
Trent McConaghy
Pieter Palmers
Georges G E Gielen
Michiel Steyaert
Citations