Adaptive Test Selection for Factorization-based Surrogate Fitness in Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{liskowski17adaptive,
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author = "Krzysztof Krawiec and Pawel Liskowski",
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title = "Adaptive Test Selection for Factorization-based
Surrogate Fitness in Genetic Programming",
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journal = "Foundations of Computing and Decision Sciences",
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year = "2017",
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volume = "42",
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number = "4",
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pages = "339--358",
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month = dec,
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keywords = "genetic algorithms, genetic programming, matrix
factorization, surrogate fitness, testbased problems,
recommender systems",
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ISSN = "0867-6356",
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URL = "https://content.sciendo.com/downloadpdf/journals/fcds/42/4/article-p339.xml",
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DOI = "doi:10.1515/fcds-2017-0017",
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size = "20 pages",
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abstract = "Genetic programming (GP) is a variant of evolutionary
algorithm where the entities undergoing simulated
evolution are computer programs. A fitness function in
GP is usually based on a set of tests, each of which
defines the desired output a correct program should
return for an exemplary input. The outcomes of
interactions between programs and tests in GP can be
represented as an interaction matrix, with rows
corresponding to programs in the current population and
columns corresponding to tests. In previous work, we
proposed SFIMX, a method that performs only a fraction
of interactions and employs non-negative matrix
factorization to estimate the outcomes of remaining
ones, shortening GP runtime. we build upon that work
and propose three extensions of SFIMX, in which the
subset of tests drawn to perform interactions is
selected with respect to test difficulty. The conducted
experiment indicates that the proposed extensions
surpass the original SFIMX on a suite of discrete GP
benchmarks.",
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notes = "FCDS The Journal of Poznan University of Technology
Institute of Computing Science, Poznan, Poland",
- }
Genetic Programming entries for
Krzysztof Krawiec
Pawel Liskowski
Citations