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POET: an evo-devo method to optimize the weights of a large artificial neural networks

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conference contribution
posted on 18.03.2015, 13:34 authored by Alessandro Fontana, Andrea SoltoggioAndrea Soltoggio, Borys Wrobel
Large search spaces as those of artificial neural networks are difficult to search with machine learning techniques. The large amount of parameters is the main challenge for search techniques that do not exploit correlations expressed as patterns in the parameter space. Evolutionary computation with indirect genotype-phenotype mapping was proposed as a possible solution, but current methods often fail when the space is fractured and presents irregularities. This study employs an evolutionary indirect encoding inspired by developmental biology. Cellular proliferations and deletions of variable size allow for the definition of both regular large areas and small detailed areas in the parameter space. The method is tested on the search of the weights of a neural network for the classification of the MNIST dataset. The results demonstrate that even large networks such as those required for image classification can be effectively automatically designed by the proposed evolutionary developmental method. The combination of real-world problems like vision and classification, evolution and development, endows the proposed method with aspects of particular relevance to artificial life.

Funding

This work was supported by the Polish National Science Center (project BIOMERGE, 2011/03/B/ST6/00399). This work was also partially supported by the European Communitys Seventh Framework Programme FP7/2007-2013, Challenge 2 Cognitive Systems, Interaction, Robotics under grant agreement No 248311 - AMARSi.

History

School

  • Science

Department

  • Computer Science

Published in

Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFE XIV). Cambridge, MA: MIT Press, 2014

Pages

1 - 8

Citation

FONTANA, A., SOLTOGGIO, A. and WROBEL, B., 2014. POET: an evo-devo method to optimize the weights of a large artificial neural networks. IN: Artificial Life 14: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFE XIV). Cambridge, MA: MIT Press, pp. 447-454.

Publisher

MIT Press

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2014

Notes

This is a conference paper.

Language

en

Location

New York, USA

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