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Surrogate Fitness via Factorization of Interaction Matrix

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Genetic Programming (EuroGP 2016)

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Abstract

We propose SFIMX, a method that reduces the number of required interactions between programs and tests in genetic programming. SFIMX 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, SFIMX attains higher success rate of synthesizing correct programs within a given computational budget.

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Acknowledgements

P. Liskowski acknowledges support from grant 2014/15/N/ST6/04572 funded by the National Science Centre, Poland.

K. Krawiec acknowledges support from grant 2014/15/B/ST6/05205 funded by the National Science Centre, Poland.

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Correspondence to Paweł Liskowski .

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Liskowski, P., Krawiec, K. (2016). Surrogate Fitness via Factorization of Interaction Matrix. In: Heywood, M., McDermott, J., Castelli, M., Costa, E., Sim, K. (eds) Genetic Programming. EuroGP 2016. Lecture Notes in Computer Science(), vol 9594. Springer, Cham. https://doi.org/10.1007/978-3-319-30668-1_5

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  • DOI: https://doi.org/10.1007/978-3-319-30668-1_5

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