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’s runtime. In this paper,

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.