research-article

Neuroevolution on the edge of chaos

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Filip MatznerAuthors Info & Claims

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference

Pages 465 - 472

https://doi.org/10.1145/3071178.3071292

Published: 01 July 2017 Publication History

Abstract

Echo state networks represent a special type of recurrent neural networks. Recent papers stated that the echo state networks maximize their computational performance on the transition between order and chaos, the so-called edge of chaos. This work confirms this statement in a comprehensive set of experiments. Furthermore, the echo state networks are compared to networks evolved via neuroevolution. The evolved networks outperform the echo state networks, however, the evolution consumes significant computational resources. It is demonstrated that echo state networks with local connections combine the best of both worlds, the simplicity of random echo state networks and the performance of evolved networks. Finally, it is shown that evolution tends to stay close to the ordered side of the edge of chaos.

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Cited By

Basterrech SRubino G(2023)Evolutionary Echo State NetworkApplied Soft Computing10.1016/j.asoc.2023.110463144:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110463
Basterrech SSharma T(2023)Re-visiting Reservoir Computing Architectures Optimized by Evolutionary AlgorithmsProceedings of the 14th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2022)10.1007/978-3-031-27524-1_81(832-843)Online publication date: 28-Mar-2023
https://doi.org/10.1007/978-3-031-27524-1_81
Matzner FWagner M(2022)Hyperparameter tuning in echo state networksProceedings of the Genetic and Evolutionary Computation Conference10.1145/3512290.3528721(404-412)Online publication date: 8-Jul-2022
https://dl.acm.org/doi/10.1145/3512290.3528721
Show More Cited By

Index Terms

Neuroevolution on the edge of chaos
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Mathematics of computing
  1. Information theory
  2. Probability and statistics
    1. Statistical paradigms
      1. Time series analysis

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cover image ACM Conferences

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference

July 2017

1427 pages

ISBN:9781450349208

DOI:10.1145/3071178

General Chair:
Peter A. N. Bosman
Centrum Wiskunde & Informatica (CWI)

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

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Published: 01 July 2017

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Specifický vysokoakolský výzkum, Univerzita Karlova
Grantová Agentura, Univerzita Karlova

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GECCO '17

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SIGEVO

GECCO '17: Genetic and Evolutionary Computation Conference

July 15 - 19, 2017

Berlin, Germany

Acceptance Rates

GECCO '17 Paper Acceptance Rate 178 of 462 submissions, 39%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Basterrech SRubino G(2023)Evolutionary Echo State NetworkApplied Soft Computing10.1016/j.asoc.2023.110463144:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110463
Basterrech SSharma T(2023)Re-visiting Reservoir Computing Architectures Optimized by Evolutionary AlgorithmsProceedings of the 14th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2022)10.1007/978-3-031-27524-1_81(832-843)Online publication date: 28-Mar-2023
https://doi.org/10.1007/978-3-031-27524-1_81
Matzner FWagner M(2022)Hyperparameter tuning in echo state networksProceedings of the Genetic and Evolutionary Computation Conference10.1145/3512290.3528721(404-412)Online publication date: 8-Jul-2022
https://dl.acm.org/doi/10.1145/3512290.3528721
Basterrech SRubino G(2022)Evolutionary Echo State Network: evolving reservoirs in the Fourier space2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892892(1-8)Online publication date: 18-Jul-2022
https://doi.org/10.1109/IJCNN55064.2022.9892892
Leysen HWalter DChristiaenssen BVandoren RHarputluoğlu İVan Loon NMaudsley S(2021)GPCRs Are Optimal Regulators of Complex Biological Systems and Orchestrate the Interface between Health and DiseaseInternational Journal of Molecular Sciences10.3390/ijms22241338722:24(13387)Online publication date: 13-Dec-2021
https://doi.org/10.3390/ijms222413387
Dale MMiller JStepney STrefzer M(2021)Reservoir Computing in Material SubstratesReservoir Computing10.1007/978-981-13-1687-6_7(141-166)Online publication date: 6-Aug-2021
https://doi.org/10.1007/978-981-13-1687-6_7
Khoury JParrikar O(2019)Search optimization, funnel topography, and dynamical criticality on the string landscapeJournal of Cosmology and Astroparticle Physics10.1088/1475-7516/2019/12/0142019:12(014-014)Online publication date: 4-Dec-2019
https://doi.org/10.1088/1475-7516/2019/12/014
Dale MTakadama K(2018)Neuroevolution of hierarchical reservoir computersProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205520(410-417)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3205455.3205520
Zhang GZhang CLi ZZhang W(2018)A New PSOGSA Inspired Convolutional Echo State Network for Long-term Health Status Prediction2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)10.1109/ROBIO.2018.8665099(1298-1303)Online publication date: Dec-2018
https://doi.org/10.1109/ROBIO.2018.8665099

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