research-article

A comparison of genetic regulatory network dynamics and encoding

Authors:

Dennis G Wilson,

Sylvain Cussat-Blanc,

Stéphane Sanchez,

Yves DuthenAuthors Info & Claims

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

Pages 91 - 98

https://doi.org/10.1145/3071178.3071322

Published: 01 July 2017 Publication History

Abstract

Genetic Regulatory Networks (GRNs) implementations have a high degree of variability in their details. Parameters, encoding methods, and dynamics formulas all differ in the literature, and some GRN implementations have a high degree of model complexity. In this paper, we present a comparative study of different implementations of a GRN and introduce new variants for comparison. We use a modified Genetic Algorithm (GA) to evaluate GRN performance on a number of common benchmark tasks, with a focus on real-time control problems. We propose an encoding scheme and set of dynamics equations that simplifies implementation and evaluate the evolutionary fitness of this proposed method. Lastly, we use the comparative modifications study to demonstrate overall enhancements for GRN models.

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Cited By

Templier PRachelson EWilson DKrawiec K(2021)A geometric encoding for neural network evolutionProceedings of the Genetic and Evolutionary Computation Conference10.1145/3449639.3459361(919-927)Online publication date: 26-Jun-2021
https://dl.acm.org/doi/10.1145/3449639.3459361
Cussat-Blanc SHarrington KBanzhaf W(2019)Artificial gene regulatory networks-a reviewArtificial Life10.1162/artl_a_0026724:4(296-328)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1162/artl_a_00267
Fernández Pérez ISanchez S(2019)Influence of Local Selection and Robot Swarm Density on the Distributed Evolution of GRNsTheory and Applications of Models of Computation10.1007/978-3-030-16692-2_38(567-582)Online publication date: 30-Mar-2019
https://doi.org/10.1007/978-3-030-16692-2_38

Index Terms

A comparison of genetic regulatory network dynamics and encoding
1. Computing methodologies
  1. Artificial intelligence
  2. Modeling and simulation
    1. Model development and 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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GECCO '17

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SIGEVO

GECCO '17: Genetic and Evolutionary Computation Conference

July 15 - 19, 2017

Berlin, Germany

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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

Templier PRachelson EWilson DKrawiec K(2021)A geometric encoding for neural network evolutionProceedings of the Genetic and Evolutionary Computation Conference10.1145/3449639.3459361(919-927)Online publication date: 26-Jun-2021
https://dl.acm.org/doi/10.1145/3449639.3459361
Cussat-Blanc SHarrington KBanzhaf W(2019)Artificial gene regulatory networks-a reviewArtificial Life10.1162/artl_a_0026724:4(296-328)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1162/artl_a_00267
Fernández Pérez ISanchez S(2019)Influence of Local Selection and Robot Swarm Density on the Distributed Evolution of GRNsTheory and Applications of Models of Computation10.1007/978-3-030-16692-2_38(567-582)Online publication date: 30-Mar-2019
https://doi.org/10.1007/978-3-030-16692-2_38

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