Scalable Symbolic Regression by Continuous Evolution with Very Small Populations
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InCollection{Smits:2010:GPTP,
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author = "Guido F. Smits and Ekaterina Vladislavleva and
Mark E. Kotanchek",
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title = "Scalable Symbolic Regression by Continuous Evolution
with Very Small Populations",
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booktitle = "Genetic Programming Theory and Practice VIII",
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year = "2010",
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editor = "Rick Riolo and Trent McConaghy and
Ekaterina Vladislavleva",
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series = "Genetic and Evolutionary Computation",
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volume = "8",
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address = "Ann Arbor, USA",
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month = "20-22 " # may,
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publisher = "Springer",
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chapter = "9",
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pages = "147--160",
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keywords = "genetic algorithms, genetic programming, symbolic
regression, continuous evolution, parallel computing,
evolvability",
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isbn13 = "978-1-4419-7746-5",
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URL = "http://www.springer.com/computer/ai/book/978-1-4419-7746-5",
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DOI = "doi:10.1007/978-1-4419-7747-2_9",
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abstract = "The future of computing is one of massive parallelism.
To exploit this and generatemaximumperformance itwill
be inevitable thatmore co-design between hardware and
software takes place. Many software algorithms need
rethinking to expose all the possible concurrency,
increase locality and have built-in fault tolerance.
Evolutionary algorithms are naturally parallel and
should as such have an edge in exploiting these
hardware features.
In this paper we try to rethink the way we implement
symbolic regression via genetic programming with the
aimto obtainmaximumscalability to architectures with a
very large number of processors. Working with very
small populations might be an important feature to
obtain a better locality of the computations. We show
that quite reasonable results can be obtained with
single chromosome crawlers and a diverse set of
mutation-only operators. Next we show that it is
possible to introduce a mechanism for constant
innovation using very small population sizes. By
introducing a computation, with competition for
cpu-cycles based on the fitness and the activity of an
individual, we can get continuous evolution within the
same cpu-budget as the single chromosome crawlers.
These results are obtained on a real life industrial
dataset with composition data from a distillation tower
with 23 potential inputs and 5000 records.",
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notes = "part of \cite{Riolo:2010:GPTP}",
- }
Genetic Programming entries for
Guido F Smits
Ekaterina (Katya) Vladislavleva
Mark Kotanchek
Citations