scholarly journals Structure Selection for Convolutive Non-negative Matrix Factorization Using Normalized Maximum Likelihood Coding

2016 ◽  
Author(s):  
Atsushi Suzuki ◽  
Kohei Miyaguchi ◽  
Kenji Yamanishi
Keyword(s):  
Maximum Likelihood ◽  
Matrix Factorization ◽  
Structure Selection ◽  
Normalized Maximum Likelihood ◽  
Selection For ◽  
Non Negative Matrix Factorization

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

2017 ◽  
Vol 42 (4) ◽  
pp. 339-358 ◽  
Author(s):  
Krzysztof Krawiec ◽  
Paweł Liskowski
Keyword(s):  
Genetic Programming ◽  
Evolutionary Algorithm ◽  
Matrix Factorization ◽  
Fitness Function ◽  
Interaction Matrix ◽  
Simulated Evolution ◽  
Adaptive Test ◽  
Selection For ◽  
Correct Program ◽  
Non Negative Matrix Factorization

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.

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