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Erschienen in:
Extreme Accuracy in Symbolic Regression Kapitel lesen Erstes Kapitel lesen

2014 | OriginalPaper | Buchkapitel

11. Geometric Semantic Genetic Programming for Real Life Applications

verfasst von : Leonardo Vanneschi, Sara Silva, Mauro Castelli, Luca Manzoni

Erschienen in: Genetic Programming Theory and Practice XI

Verlag: Springer New York

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Abstract

In a recent contribution we have introduced a new implementation of geometric semantic operators for Genetic Programming. Thanks to this implementation, we are now able to deeply investigate their usefulness and study their properties on complex real-life applications. Our experiments confirm that these operators are more effective than traditional ones in optimizing training data, due to the fact that they induce a unimodal fitness landscape. Furthermore, they automatically limit overfitting, something we had already noticed in our recent contribution, and that is further discussed here. Finally, we investigate the influence of some parameters on the effectiveness of these operators, and we show that tuning their values and setting them “a priori” may be wasted effort. Instead, if we randomly modify the values of those parameters several times during the evolution, we obtain a performance that is comparable with the one obtained with the best setting, both on training and test data for all the studied problems.

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Vorheriges Kapitel Gaining Deeper Insights in Symbolic Regression
Nächstes Kapitel Evaluation of Parameter Contribution to Neural Network Size and Fitness in ATHENA for Genetic Analysis
Fußnoten
1
Both in the text and in the pseudocode of Fig. 11.2, we abuse the term “ancestors” to designate not only the parents but also the random trees used to build an individual by crossover or mutation.
 
2
Figure 11.1b makes an assumption: that the number of instances of the test set is identical to the one of the training set. Otherwise, the training target T and the test point τ could not be drawn in the same plane, because they would have different dimensions. This hypothesis is false in general, but it is used for simplicity, since it helps us to explain more clearly our hypothesis. Nevertheless, it is worth pointing out that, of course, the argument holds also when this restrictive assumption is false.
 
Literatur
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Metadaten
Titel
Geometric Semantic Genetic Programming for Real Life Applications
verfasst von
Leonardo Vanneschi
Sara Silva
Mauro Castelli
Luca Manzoni
Copyright-Jahr
2014
Verlag
Springer New York
Buch
Genetic Programming Theory and Practice XI

Print ISBN: 978-1-4939-0374-0

Electronic ISBN: 978-1-4939-0375-7

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-1-4939-0375-7_11

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