Surrogate Fitness via Factorization of Interaction Matrix

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{Liskowski:2016:EuroGP,

• author = "Pawel Liskowski and Krzysztof Krawiec",
• title = "Surrogate Fitness via Factorization of Interaction Matrix",
• booktitle = "EuroGP 2016: Proceedings of the 19th European Conference on Genetic Programming",
• year = "2016",
• month = "30 " # mar # "--1 " # apr,
• editor = "Malcolm I. Heywood and James McDermott and Mauro Castelli and Ernesto Costa and Kevin Sim",
• series = "LNCS",
• volume = "9594",
• publisher = "Springer Verlag",
• address = "Porto, Portugal",
• pages = "68--82",
• organisation = "EvoStar",
• keywords = "genetic algorithms, genetic programming, test-based problem, recommender systems, machine learning, surrogate fitness",
• isbn13 = "978-3-319-30668-1",
• DOI = "doi:10.1007/978-3-319-30668-1_5",
• abstract = "We propose \mname, a method that reduces the number of required interactions between programs and tests in genetic programming. \mname performs factorization of the matrix of the outcomes of interactions between the programs in a working population and the tests. Crucially, that factorization is applied to matrix that is only partially filled with interaction outcomes, i.e., sparse. The reconstructed approximate interaction matrix is then used to calculate the fitness of programs. In empirical comparison to several reference methods in categorical domains, \mname attains higher success rate of synthesizing correct programs within a given computational budget.",
• notes = "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in conjunction with EvoCOP2016, EvoMusArt2016 and EvoApplications2016",

}

Genetic Programming entries for Pawel Liskowski Krzysztof Krawiec

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