Better Solutions Faster: Soft Evolution of Robust Regression Models In Pareto genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InCollection{Vladislavleva:2007:GPTP,
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author = "Ekaterina Vladislavleva and Guido Smits and
Mark Kotanchek",
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title = "Better Solutions Faster: Soft Evolution of Robust
Regression Models In Pareto genetic programming",
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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 = "2",
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pages = "13--32",
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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_2",
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size = "19 pages",
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abstract = "Better solutions faster is the reality of the
industrial modelling world, now more than ever.
Efficiency requirements, market pressures, and ever
changing data force us to use symbolic regression via
genetic programming (GP) in a highly automated fashion.
This is why we want our GP system to produce simple
solutions of the highest possible quality with the
lowest computational effort, and a high consistency in
the results of independent GP runs. In this chapter, we
show that genetic programming with a focus on ranking
in combination with goal softening is a very powerful
way to improve the efficiency and effectiveness of the
evolutionary search. Our strategy consists of partial
fitness evaluations of individuals on random subsets of
the original data set, with a gradual increase in the
subset size in consecutive generations. From a series
of experiments performed on three test problems, we
observed that those evolutions that started from the
smallest subset sizes (10percent) consistently led to
results that are superior in terms of the goodness of
fit, consistency between independent runs, and
computational effort. Our experience indicates that
solutions obtained using this approach are also less
complex and more robust against over-fitting. We find
that the near-optimal strategy of allocating
computational budget over a GP run is to evenly
distribute it over all generations. This implies that
initially, more individuals can be evaluated using
small subset sizes, promoting better exploration.
Exploitation becomes more important towards the end of
the run, when all individuals are evaluated using the
full data set with correspondingly smaller population
sizes.",
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notes = "part of \cite{Riolo:2007:GPTP} published 2008",
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affiliation = "Tilburg University Tilburg The Netherlands",
- }
Genetic Programming entries for
Ekaterina (Katya) Vladislavleva
Guido F Smits
Mark Kotanchek
Citations