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Employing a Restricted Set of Qualitative Relations in Recognizing Plain Sketches Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Parametrizing Cartesian Genetic Programming: An Empirical Study

Authors : Paul Kaufmann, Roman Kalkreuth

Published in: KI 2017: Advances in Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

Since its introduction two decades ago, the way researchers parameterized and optimized Cartesian Genetic Programming (CGP) remained almost unchanged. In this work we investigate non-standard parameterizations and optimization algorithms for CGP. We show that the conventional way of using CGP, i.e. configuring it as a single line optimized by an (1+4) Evolutionary Strategies-style search scheme, is a very good choice but that rectangular CGP geometries and more elaborate metaheuristics, such as Simulated Annealing, can lead to faster convergence rates.

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Metadata
Title
Parametrizing Cartesian Genetic Programming: An Empirical Study
Authors
Paul Kaufmann
Roman Kalkreuth
Copyright Year
2017
Book
KI 2017: Advances in Artificial Intelligence

Print ISBN: 978-3-319-67189-5

Electronic ISBN: 978-3-319-67190-1

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-67190-1_26

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