Using Co-solvability to Model and Exploit Synergetic Effects in Evolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{Krawiec:ppsn2010,
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author = "Krzysztof Krawiec and Pawel Lichocki",
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title = "Using Co-solvability to Model and Exploit Synergetic
Effects in Evolution",
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booktitle = "PPSN 2010 11th International Conference on Parallel
Problem Solving From Nature",
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pages = "492--501",
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year = "2010",
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volume = "6239",
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editor = "Robert Schaefer and Carlos Cotta and
Joanna Kolodziej and Guenter Rudolph",
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publisher = "Springer",
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series = "Lecture Notes in Computer Science",
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isbn13 = "978-3-642-15870-4",
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address = "Krakow, Poland",
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month = "11-15 " # sep,
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1007/978-3-642-15871-1_50",
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abstract = "We introduce, analyse, and experimentally examine
co-solvability, an ability of a solution to solve a
pair of fitness cases (tests). Based on this concept,
we devise a co-solvability fitness function that makes
solutions compete for rewards granted for solving pairs
of tests, in a way analogous to implicit fitness
sharing. We prove that co-solvability fitness function
is by definition synergistic and imposes selection
pressure which is qualitatively different from that of
standard fitness function or implicit fitness sharing.
The results of experimental verification on eight
genetic programming tasks demonstrate that evolutionary
runs driven by co-solvability fitness function usually
converge faster to well-performing solutions and are
more likely to reach global optima.",
- }
Genetic Programming entries for
Krzysztof Krawiec
Pawel Lichocki
Citations