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Surrogate Fitness via Factorization of Interaction Matrix

  • Paweł LiskowskiEmail author
  • Krzysztof Krawiec
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9594)

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.

Keywords

Genetic programming Test-based problem Recommender systems Machine learning Surrogate fitness 

Notes

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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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Computing SciencePoznan University of TechnologyPoznańPoland

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