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Adaptive Test Selection for Factorization-based Surrogate Fitness in Genetic Programming


[ 1 ] Instytut Informatyki, Wydział Informatyki, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication


Published in

Foundations of Computing and Decision Sciences

Journal year: 2017 | Journal volume: vol. 42 | Journal number: no. 4

Article type

scientific article

Publication language


  • genetic programming
  • matrix factorization
  • surrogate fitness
  • test-based problems
  • recommender systems

EN 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 per-forms only a fraction of interactions and employs non-negative matrix factorization to estimate the outcomes of remaining ones, shortening GP’s runtime. In this paper, we build upon that work and propose three extensions of SFIMX, in which the sub-set 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.

Date of online publication


Pages (from - to)

339 - 358




License type

CC BY-NC-ND (attribution - noncommercial - no derivatives)

Open Access Mode

publisher's website

Open Access Text Version

final published version

Date of Open Access to the publication

at the time of publication

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Ministry points / journal in years 2017-2021


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