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One-Class Classification for Anomaly Detection with Kernel Density Estimation and Genetic Programming Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Surrogate Fitness via Factorization of Interaction Matrix

Authors : Paweł Liskowski, Krzysztof Krawiec

Published in: Genetic Programming

Publisher: Springer International Publishing

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Abstract

We propose SFIMX, a method that reduces the number of required interactions between programs and tests in genetic programming. SFIMX performs factorization of the matrix of the outcomes of interactions between the programs in a working population and the tests. Crucially, that factorization is applied to matrix that is only partially filled with interaction outcomes, i.e., sparse. The reconstructed approximate interaction matrix is then used to calculate the fitness of programs. In empirical comparison to several reference methods in categorical domains, SFIMX attains higher success rate of synthesizing correct programs within a given computational budget.

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Metadata
Title
Surrogate Fitness via Factorization of Interaction Matrix
Authors
Paweł Liskowski
Krzysztof Krawiec
Copyright Year
2016
Book
Genetic Programming

Print ISBN: 978-3-319-30667-4

Electronic ISBN: 978-3-319-30668-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-30668-1_5

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