<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></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’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.

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

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Genetic programming (GP) is a variant of evolutionary algorithm where the entities undergoing simulated evolution are computer programs. A fitness function in...

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

Pubblicato online: 09 dic 2017

Pagine: 339 - 358

Ricevuto: 12 apr 2017

Accettato: 23 ago 2017

DOI: https://doi.org/10.1515/fcds-2017-0017

Parole chiave
genetic programming, matrix factorization, surrogate fitness, testbased problems, recommender systems

© by Paweł Liskowski

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Pubblicato online: 09 dic 2017

Pagine: 339 - 358

Ricevuto: 12 apr 2017

Accettato: 23 ago 2017

DOI: https://doi.org/10.1515/fcds-2017-0017

Parole chiavegenetic programming, matrix factorization, surrogate fitness, testbased problems, recommender systems

© by Paweł Liskowski

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
genetic programming, matrix factorization, surrogate fitness, testbased problems, recommender systems