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

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

@Article{liskowski17adaptive,

• author = "Krzysztof Krawiec and Pawel Liskowski",
• title = "Adaptive Test Selection for Factorization-based Surrogate Fitness in Genetic Programming",
• journal = "Foundations of Computing and Decision Sciences",
• year = "2017",
• volume = "42",
• number = "4",
• pages = "339--358",
• month = dec,
• keywords = "genetic algorithms, genetic programming, matrix factorization, surrogate fitness, testbased problems, recommender systems",
• ISSN = "0867-6356",
• URL = "https://content.sciendo.com/downloadpdf/journals/fcds/42/4/article-p339.xml",
• DOI = "doi:10.1515/fcds-2017-0017",
• size = "20 pages",
• abstract = "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 runtime. 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.",
• notes = "FCDS The Journal of Poznan University of Technology Institute of Computing Science, Poznan, Poland",

}

Genetic Programming entries for Krzysztof Krawiec Pawel Liskowski

Citations