Evolutionary construction of convolutional neural networks

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Neuro-Evolution is a field of study that has recently gained significantly increased traction in the deep learning community. It combines deep neural networks and evolutionary algorithms to improve and/or automate the construction of neural networks. Recent Neuro-Evolution approaches have shown promising results, rivaling hand-crafted neural networks in terms of accuracy.
A two-step approach is introduced where a convolutional autoencoder is created that efficiently compresses the input data in the first step, and a convolutional neural network is created to classify the compressed data in the second step. The creation of networks in both steps is guided by by an evolutionary process, where new networks are constantly being generated by mutating members of a collection of existing networks. Additionally, a method is introduced that considers the trade-off between compression and information loss of different convolutional autoencoders. This is used to select the optimal convolutional autoencoder from among those evolved to compress the data for the second step.
The complete framework is implemented, tested on the popular CIFAR-10 data set, and the results are discussed. Finally, a number of possible directions for future work with this particular framework in mind are considered, including opportunities to improve its efficiency and its application in particular areas.
Originele taal-2Engels
TitelMachine Learning, Optimization, and Data Science - 4th International Conference, LOD 2018, Revised Selected Papers
RedacteurenGiuseppe Nicosia, Giovanni Giuffrida, Giuseppe Nicosia, Panos Pardalos, Vincenzo Sciacca, Renato Umeton
Plaats van productieCham
UitgeverijSpringer
Pagina's293-304
Aantal pagina's12
ISBN van elektronische versie978-3-030-13709-0
ISBN van geprinte versie978-3-030-13708-3
DOI's
StatusGepubliceerd - feb 2019
Evenement4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018 - Volterra, Italië
Duur: 13 sep 201816 sep 2018

Publicatie series

NaamLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11331 LNCS
ISSN van geprinte versie0302-9743
ISSN van elektronische versie1611-3349

Congres

Congres4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018
LandItalië
StadVolterra
Periode13/09/1816/09/18

Vingerafdruk

Neuroevolution
Neural Networks
Neural networks
Evolutionary algorithms
Information Loss
Network Algorithms
Evolutionary Algorithms
Compression
Trade-offs
Classify
Framework
Deep neural networks
Deep learning
Learning
Community

Citeer dit

van Knippenberg, M. S., Menkovski, V., & Consoli, S. (2019). Evolutionary construction of convolutional neural networks. In G. Nicosia, G. Giuffrida, G. Nicosia, P. Pardalos, V. Sciacca, & R. Umeton (editors), Machine Learning, Optimization, and Data Science - 4th International Conference, LOD 2018, Revised Selected Papers (blz. 293-304). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11331 LNCS). Cham: Springer. https://doi.org/10.1007/978-3-030-13709-0_25
van Knippenberg, M.S. ; Menkovski, V. ; Consoli, Sergio. / Evolutionary construction of convolutional neural networks. Machine Learning, Optimization, and Data Science - 4th International Conference, LOD 2018, Revised Selected Papers. redacteur / Giuseppe Nicosia ; Giovanni Giuffrida ; Giuseppe Nicosia ; Panos Pardalos ; Vincenzo Sciacca ; Renato Umeton. Cham : Springer, 2019. blz. 293-304 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{d38584dfd23e4f839d90481db43a95ff,
title = "Evolutionary construction of convolutional neural networks",
abstract = "Neuro-Evolution is a field of study that has recently gained significantly increased traction in the deep learning community. It combines deep neural networks and evolutionary algorithms to improve and/or automate the construction of neural networks. Recent Neuro-Evolution approaches have shown promising results, rivaling hand-crafted neural networks in terms of accuracy.A two-step approach is introduced where a convolutional autoencoder is created that efficiently compresses the input data in the first step, and a convolutional neural network is created to classify the compressed data in the second step. The creation of networks in both steps is guided by by an evolutionary process, where new networks are constantly being generated by mutating members of a collection of existing networks. Additionally, a method is introduced that considers the trade-off between compression and information loss of different convolutional autoencoders. This is used to select the optimal convolutional autoencoder from among those evolved to compress the data for the second step.The complete framework is implemented, tested on the popular CIFAR-10 data set, and the results are discussed. Finally, a number of possible directions for future work with this particular framework in mind are considered, including opportunities to improve its efficiency and its application in particular areas.",
keywords = "Convolutional autoencoders, Convolutional neural networks, Genetic algorithms, Neuro-evolution",
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van Knippenberg, MS, Menkovski, V & Consoli, S 2019, Evolutionary construction of convolutional neural networks. in G Nicosia, G Giuffrida, G Nicosia, P Pardalos, V Sciacca & R Umeton (redactie), Machine Learning, Optimization, and Data Science - 4th International Conference, LOD 2018, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11331 LNCS, Springer, Cham, blz. 293-304, Volterra, Italië, 13/09/18. https://doi.org/10.1007/978-3-030-13709-0_25

Evolutionary construction of convolutional neural networks. / van Knippenberg, M.S.; Menkovski, V.; Consoli, Sergio.

Machine Learning, Optimization, and Data Science - 4th International Conference, LOD 2018, Revised Selected Papers. redactie / Giuseppe Nicosia; Giovanni Giuffrida; Giuseppe Nicosia; Panos Pardalos; Vincenzo Sciacca; Renato Umeton. Cham : Springer, 2019. blz. 293-304 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11331 LNCS).

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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van Knippenberg MS, Menkovski V, Consoli S. Evolutionary construction of convolutional neural networks. In Nicosia G, Giuffrida G, Nicosia G, Pardalos P, Sciacca V, Umeton R, redacteurs, Machine Learning, Optimization, and Data Science - 4th International Conference, LOD 2018, Revised Selected Papers. Cham: Springer. 2019. blz. 293-304. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-13709-0_25