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Erschienen in:
Using GP Is NEAT: Evolving Compositional Pattern Production Functions Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Structurally Layered Representation Learning: Towards Deep Learning Through Genetic Programming

verfasst von : Lino Rodriguez-Coayahuitl, Alicia Morales-Reyes, Hugo Jair Escalante

Erschienen in: Genetic Programming

Verlag: Springer International Publishing

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Abstract

We introduce a novel method for representation learning based on genetic programming (GP). Inspired into the way that deep neural networks learn descriptive/discriminative representations from raw data, we propose a structurally layered representation that allows GP to learn a feature space from large scale and high dimensional data sets. Previous efforts from the GP community for feature learning have focused on small data sets with a few input variables, also, most approaches rely on domain expert knowledge to produce useful representations. In this paper, we introduce the structurally layered GP formulation, together with an efficient scheme to explore the search space and show that this framework can be used to learn representations from large data sets of high dimensional raw data. As case of study we describe the implementation and experimental evaluation of an autoencoder developed under the proposed framework. Results evidence the benefits of the proposed framework and pave the way for the development of deep genetic programming.

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Vorheriges Kapitel Investigating a Machine Breakdown Genetic Programming Approach for Dynamic Job Shop Scheduling
Nächstes Kapitel Comparing Rule Evaluation Metrics for the Evolutionary Discovery of Multi-relational Association Rules in the Semantic Web
Fußnoten
1
Please note that reducing the dimensionality is not necessarily a requirement of representation learning, but herein we include this restriction so that the learned representations can be descriptive/discriminative and compact at the same time.
 
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Metadaten
Titel
Structurally Layered Representation Learning: Towards Deep Learning Through Genetic Programming
verfasst von
Lino Rodriguez-Coayahuitl
Alicia Morales-Reyes
Hugo Jair Escalante
Copyright-Jahr
2018
Buch
Genetic Programming

Print ISBN: 978-3-319-77552-4

Electronic ISBN: 978-3-319-77553-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-77553-1_17