skip to main content
10.1145/1276958.1277162acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
Article

A common genetic encoding for both direct and indirect encodings of networks

Published: 07 July 2007 Publication History

Abstract

In this paper we present a Common Genetic Encoding (CGE) for networks that can be applied to both direct and indirect encoding methods. As a direct encoding method, CGE allows the implicit evaluation of an encoded phenotype without the need to decode the phenotype from the genotype. On the other hand, one can easily decode the structure of a phenotype network, since its topology is implicitly encoded in the genotype's gene-order. Furthermore, we illustrate how CGE can be used for the indirect encoding of networks. CGE has useful properties that makes it suitable for evolving neural networks. A formal definition of the encoding is given, and some of the important properties of the encoding are proven such as its closure under mutation operators, its completeness in representing any phenotype network, and the existence of an algorithm that can evaluate any given phenotype without running into an infinite loop.

References

[1]
P. J. Angeline, G. M. Saunders, and J. B. Pollack. An evolutionary algorithm that constructs recurrent neural networks. IEEE Transactions on Neural Networks, 5:54--65, 1994.
[2]
F. Gomez, J. Schmidhuber, and R. Miikkulainen. Efficient non-linear control through neuroevolution. In Proceedings of the European Conference on Machine Learning (ECML 2006), 2006.
[3]
F. Gruau. Neural Network Synthesis Using Cellular Encoding and the Genetic Algorithm. PhD thesis, Ecole Normale Superieure de Lyon, Laboratoire de l'Informatique du Parallelisme, France, January 1994.
[4]
Y. Kassahun. Towards a Unified Approach to Learning and Adaptation. PhD thesis, Technical Report 0602, Institute of Computer Science and Applied Mathematics, Christian-Albrechts University, Kiel, Germany, February 2006.
[5]
S. Luke and L. Spector. Evolving graphs and networks with edge encoding: Preliminary report. In Late-breaking papers of Genetic Programming 1996. Stanford, CA, 1996.
[6]
S. Nolfi and D. Floreano. Evolutionary Robotics. The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press, Massachusetts, London, 2000.
[7]
J. Reisinger, K. O. Stanley, and R. Miikkulainen. Evolving reusable neural modules. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004), pages 69--81, 2004.
[8]
K. O. Stanley. Efficient Evolution of Neural Networks through Complexification. PhD thesis, Artificial Intelligence Laboratory. The University of Texas at Austin., Austin, USA, August 2004.
[9]
M. E. Taylor, S. Whiteson, and P. Stone. Comparing evolutionary and temporal difference methods in a reinforcement learning domain. In GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pages 1321--1328, New York, NY, USA, 2006. ACM Press.
[10]
X. Yao. Evolving artificial neural networks. Proceedings of the IEEE, 87(9):1423--1447, 1999.

Cited By

View all
  • (2023)Neat algorithm for simple games2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)10.1109/ECAI58194.2023.10193858(1-6)Online publication date: 29-Jun-2023
  • (2021)Parallel Distributed Implementation of Neuroevolution of Augmenting Topologies in Continuous Control Tasks2021 IEEE 3rd International Conference on Advanced Trends in Information Theory (ATIT)10.1109/ATIT54053.2021.9678858(267-271)Online publication date: 15-Dec-2021
  • (2021)A comprehensive survey on optimizing deep learning models by metaheuristicsArtificial Intelligence Review10.1007/s10462-021-09992-0Online publication date: 31-Mar-2021
  • Show More Cited By

Index Terms

  1. A common genetic encoding for both direct and indirect encodings of networks

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
    July 2007
    2313 pages
    ISBN:9781595936974
    DOI:10.1145/1276958
    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 ACM 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]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 07 July 2007

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. genetic encoding
    2. genotype phenotype mapping

    Qualifiers

    • Article

    Conference

    GECCO07
    Sponsor:

    Acceptance Rates

    GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)8
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 17 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2023)Neat algorithm for simple games2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)10.1109/ECAI58194.2023.10193858(1-6)Online publication date: 29-Jun-2023
    • (2021)Parallel Distributed Implementation of Neuroevolution of Augmenting Topologies in Continuous Control Tasks2021 IEEE 3rd International Conference on Advanced Trends in Information Theory (ATIT)10.1109/ATIT54053.2021.9678858(267-271)Online publication date: 15-Dec-2021
    • (2021)A comprehensive survey on optimizing deep learning models by metaheuristicsArtificial Intelligence Review10.1007/s10462-021-09992-0Online publication date: 31-Mar-2021
    • (2019)PSO-based optimized CNN for Hindi ASRInternational Journal of Speech Technology10.1007/s10772-019-09652-3Online publication date: 24-Oct-2019
    • (2019)On the automated, evolutionary design of neural networks: past, present, and futureNeural Computing and Applications10.1007/s00521-019-04160-6Online publication date: 27-Mar-2019
    • (2013)A Framework for High Performance Embedded Signal Processing and Classification of Psychophysiological DataAPCBEE Procedia10.1016/j.apcbee.2013.08.0137(60-66)Online publication date: 2013
    • (2013)Comparison of Sensor-Feedback Prediction Methods for Robust Behavior ExecutionKI 2013: Advances in Artificial Intelligence10.1007/978-3-642-40942-4_18(200-211)Online publication date: 2013
    • (2011)On Applying Neuroevolutionary Methods to Complex Robotic TasksNew Horizons in Evolutionary Robotics10.1007/978-3-642-18272-3_7(85-108)Online publication date: 2011
    • (2009)Incremental Acquisition of Neural Structures through EvolutionDesign and Control of Intelligent Robotic Systems10.1007/978-3-540-89933-4_10(187-208)Online publication date: 2009
    • (2008)Evolving Neural Networks for Online Reinforcement LearningProceedings of the 10th International Conference on Parallel Problem Solving from Nature --- PPSN X - Volume 519910.5555/2951659.2951714(518-527)Online publication date: 13-Sep-2008
    • Show More Cited By

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media