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Evolutionary curriculum learning approach for transferable cellular automata rule optimization

Authors:

Delia Dumitru,

Anca Andreica,

Laura Dioşan,

Zoltán BálintAuthors Info & Claims

GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

Pages 63 - 64

https://doi.org/10.1145/3377929.3389911

Published: 08 July 2020 Publication History

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Abstract

This paper proposes a novel method for supervised optimization of cellular automata rules using curriculum learning. The optimized edge detector manages to generalize a rule from synthetic data that is applicable to magnetic resonance images, removing the need for manual annotation of medical data. The method achieves competitive results with classical edge detectors on our test set and may be incorporated in computer-aided diagnosis systems.

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D. Dumitru et al. "Particle Swarm Optimization of Cellular Automata Rules for Edge Detection". In: 2019 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). 2019, pp. 320--325.

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Cited By

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Dumitru DDioșan LAndreica ABálint Z(2021)A Transfer Learning Approach on the Optimization of Edge Detectors for Medical Images Using Particle Swarm OptimizationEntropy10.3390/e2304041423:4(414)Online publication date: 31-Mar-2021
https://doi.org/10.3390/e23040414
Dumitru DAndreica ADioşan LBálint Z(2020)Robustness analysis of transferable cellular automata rules optimized for edge detectionProcedia Computer Science10.1016/j.procs.2020.09.044176(713-722)Online publication date: 2020
https://doi.org/10.1016/j.procs.2020.09.044

Index Terms

Evolutionary curriculum learning approach for transferable cellular automata rule optimization
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection
    2. Search methodologies
      1. Continuous space search

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Information

Published In

GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

July 2020

1982 pages

ISBN:9781450371278

DOI:10.1145/3377929

General Chair:
Carlos Artemio Coello Coello
CINVESTAV-IPN

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2020

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European Regional Development Fund

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GECCO '20

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SIGEVO

GECCO '20: Genetic and Evolutionary Computation Conference

July 8 - 12, 2020

Cancún, Mexico

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

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Dumitru DDioșan LAndreica ABálint Z(2021)A Transfer Learning Approach on the Optimization of Edge Detectors for Medical Images Using Particle Swarm OptimizationEntropy10.3390/e2304041423:4(414)Online publication date: 31-Mar-2021
https://doi.org/10.3390/e23040414
Dumitru DAndreica ADioşan LBálint Z(2020)Robustness analysis of transferable cellular automata rules optimized for edge detectionProcedia Computer Science10.1016/j.procs.2020.09.044176(713-722)Online publication date: 2020
https://doi.org/10.1016/j.procs.2020.09.044

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Cellular particle swarm optimization

Edge detection with fuzzy cellular automata transition function optimized by PSO

Uniform Cellular Automata Linear Rules for Edge Detection