Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/2463372.2463490acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
research-article

GEARNet: grammatical evolution with artificial regulatory networks

Published: 06 July 2013 Publication History

Abstract

The Central Dogma of Biology states that genes made proteins that made us. This principle has been revised in order to incorporate the role played by a multitude of regulatory mechanisms that are fundamental in both the processes of inheritance and development. Evolutionary Computation algorithms are inspired by the theories of evolution and development, but most of the computational models proposed so far rely on a simple genotype to phenotype mapping. During the last years some researchers advocate the need to explore computationally the new biological understanding and have proposed different gene expression models to be incorporated in the algorithms.Two examples are the Artificial Regulatory Network (ARN) model, first proposed by Wolfgang Banzhaf, and the Grammatical Evolution (GE) model, introduced by Michael O'Neill and Conor Ryan. In this paper, we show how a modified version of the ARN can be combined with the GE approach, in the context of automatic program generation. More precisely, we rely on the ARN to control the gene expression process ending in an ordered set of proteins, and on the GE to build, guided by a grammar, a computational structure from that set. As a proof of concept we apply the hybrid model to two benchmark problems and show that it is effective in solving them.

References

[1]
A. E. B. Eiben and J. E. Smith, Introduction to Evolutionary Computing. Springer Verlag, 2003.
[2]
W. Banzhaf, G. Beslon, S. Christensen, J. Foster, F. Képès, V. Lefort, J. Miller, M. Radman, and J. Ramsden, "From artificial evolution to computational evolution: a research agenda", Nature Reviews Genetics, vol. 7, no. 9, pp. 729--735, 2006.
[3]
D. S. Burke, K. A. De Jong, J. J. Grefenstette, C. L. Ramsey, and A. S. Wu, "Putting more genetics into genetic algorithms", Evolutionary Computation, vol. 6, no. 4, pp. 387--410, 1998.
[4]
S. Luke, S. Hamahashi, and H. Kitano, ""genetic" programming", in Genetic and Evolutionary Computation--GECCO 1999, pp. 1098--1105, 1999.
[5]
P. Nordin, "A Compiling Genetic Programming System that Directly Manipulates the Machine Code," in Advances in Genetic Programming (K. E. Kinnear, Jr., ed.), ch. 14, pp. 311--331, MIT Press, 1994.
[6]
J. F. Miller, Cartesian Genetic Programming. Natural Computing Series, Springer, 2011.
[7]
C. Ferreira, Gene Expression Programming (2nd Edition). Springer, 2006.
[8]
M. Lones and A. Tyrrell, "Biomimetic representation with genetic programming enzyme", Genetic Programming and Evolvable Machines, pp. 193--217, 2002.
[9]
J. Krohn, P. Bentley, and H. Shayani, "The challenge of irrationality: fractal protein recipes for PI", of the 11th Annual conference on Genetic and Evolutionary Computation, pp. 715--722, 2009.
[10]
W. Banzhaf, "Artificial Regulatory Networks and Genetic Programming", in Genetic Programming Theory and Practice (R. L. Riolo and B. Worzel, eds.), ch. 4, pp. 43--62, Kluwer, 2003.
[11]
P. Dwight Kuo, W. Banzhaf, and A. Leier, "Network topology and the evolution of dynamics in an artificial genetic regulatory network model created by whole genome duplication and divergence." Bio Systems, vol. 85, no. 3, pp. 177--200, 2006.
[12]
M. Nicolau, M. Schoenauer, and W. Banzhaf, "Evolving Genes to Balance a Pole", in Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010 (A. I. Esparcia-Alcazar, A. Ekart, S. Silva, S. Dignum, and A. S. Uyar, eds.), vol. 6021 of LNCS, (Istanbul), pp. 196--207, Springer, 2010.
[13]
R. L. Lopes and E. Costa, "ReNCoDe: A Regulatory Network Computational Device", in EuroGP'11: Proceedings of the 13th annual conference on Genetic and Evolutionary Computation (S. Silva, J. A. Foster, M. Nicolau, M. Giacobini, and P. Machado, eds.), vol. 6621 of LNCS, (Turin, Italy), pp. 143--154, Springer Verlag, 2011.
[14]
R. L. Lopes and E. Costa, "Using Feedback in a Regulatory Network Computational Device", in GECCO'11: Proceedings of the 13th annual conference on Genetic and Evolutionary Computation, 2011.
[15]
R. Lopes and E. Costa, "The regulatory computational device", Genetic Programming and Evolvable Machines, vol. 13, no. 3, pp. 339--375, 2012.
[16]
M. O'Neill and C. Ryan, Grammatical Evolution: Evolutionary Automatic Programming in a Arbitrary Language, vol. 4 of Genetic programming. Kluwer Academic Publishers, 2003.
[17]
H. Bolouri, Computational modeling of gene regulatory networks. Imperial College Press, 2008.
[18]
T. Reil, "Dynamics of gene expression in an artifical genome - implications for biological and artificial ontogeny", in Proceedings of the 5th European conference on Artificial Life (D. Floreano, J.-D. Nicoud, and F. Mondada, eds.), pp. 457--466, Springer, 1999.
[19]
S. Kauffman, The origins of order: self-organization and selection of evolution. Oxford University Press, 1993.
[20]
H. de Jong, J.-L. Gouzé, C. Hernandez, M. Page, T. Sari, and J. Geiselmann, "Hybrid modeling and simulation of genetic regulatory networks", in Proceedings of the 6th international conference on Hybrid systems: computation and control, HSCC'03, (Berlin, Heidelberg), pp. 267--282, Springer-Verlag, 2003.
[21]
P. Dwight Kuo, W. Banzhaf, and A. Leier, "Network topology and the evolution of dynamics in an artificial genetic regulatory network model created by whole genome duplication and divergence." Bio Systems, vol. 85, no. 3, pp. 177--200, 2006.
[22]
P. Eggenberger, "Evolving morphologies of simulated 3d organisms based on differential gene expression", in Fourth European Conference of Artificial Life (P. Husbands and I. Harvey, eds.), pp. 205--213, MIT Press, 1997.
[23]
D. Roggen, D. Federici, and D. Floreano, "Evolutionary morphogenesis for multi-cellular systems", Genetic Programming and Evolvable Machines, vol. 8, pp. 61--96, Mar. 2007.
[24]
M. Joachimczak and B. Wrobel, "Evo-devo in silico: a model of a gene network regulating multicellular development in 3d space with artificial physics," in Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems (S. Bullock, J. Noble, R. Watson, and M. A. Bedau, eds.), pp. 297--304, MIT Press, 2008.
[25]
J. Knabe, M. Schilstra, and C. Nehaviv, "Evolution and morphogenesis of differential multicellular organisms: autonomously generated diffusion gradients for positional information", in Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems (S. Bullock, J. Noble, R. Watson, and M. A. Bedau, eds.), pp. 321--328, MIT Press, 2008.
[26]
M. Kessler, Analysis of the dynamics of a GRN-based Evo-devo system. PhD thesis, University of Zurich, September 2009.
[27]
J. Bongard, "Evolving modular genetic regulatory networks", in IEEE 2002 Congress on Evolutionary Computation (CEC2002), pp. 1872--1877, IEEE Press, 2002.
[28]
T. Quick, C. Nehaviv, K. Dautenhahn, and G. Roberts, "Evolving embodied genetic regulatory network-driven control systems", in Proceedings of the European Conference on Artificial Life (ECAL 2003) (W. Banzhaf, ed.), vol. 2801 of Lecture Notes in Artificial Intelligence, pp. 266--277, 2003.
[29]
P. Kennedy and T. Osborn, "A model of gene expression and regulation in an artificial cellular organism", Complex Systems, vol. 13, no. 1, pp. 33--59, 2001.
[30]
P. Kuo, A. Leier, and W. Banzhaf, "Evolving Dynamics in an Artificial Regulatory Network Model", in Proc. of the Parallel Problem Solving from Nature Conference (PPSN-04) (X. Yao, E. Burke, J. A. Lozano, J. Smith, J. J. Merelo-Guervós, J. A. Bullinaria, J. Rowe, P. Tino, A. Kabán, and H.-P. Schwefel, eds.), pp. 571--580, Springer, 2004.
[31]
R. McKay, N. Hoai, P. Whigham, and M. O'Neil, "Genetic representation and genetic neutrality in gene expression programming", Genetic Programming and Evolvable Machines, vol. 11, no. 31, pp. 365--396, 2010.
[32]
J. Byrne, M. O'Neill, and A. Brabazon, "Structural and nodal mutation in grammatical evolution", in GECCO'09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation (G. Raidl, F. Rothlauf, G. Squillero, R. Drechsler, T. Stuetzle, M. Birattari, C. B. Congdon, M. Middendorf, C. Blum, C. Cotta, P. Bosman, J. Grahl, J. Knowles, D. Corne, H.-G. Beyer, K. Stanley, J. F. Miller, J. van Hemert, T. Lenaerts, M. Ebner, J. Bacardit, M. O'Neill, M. Di Penta, B. Doerr, T. Jansen, R. Poli, and E. Alba, eds.), (Montreal), pp. 1881--1882, ACM, 2009.
[33]
M. O'Neill, C. Ryan, M. Keijzer, and M. Cattolico, "Crossover in Grammatical Evolution", Genetic Programming and Evolvable Machines, vol. 4, pp. 67--93, Mar. 2003.
[34]
J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA, USA: MIT Press, 1992.
[35]
W. B. Langdon and R. Poli, "Why ants are hard", Cognitive Science Research Papers, pp. 193--201, Jan. 1998.
[36]
C. Ryan and M. O'Neill, "Grammatical Evolution: A Steady State Approach", in Second International Workshop on Frontiers in Evolutionary Algorithms, vol. 2, (Research Triangle Park, NC, USA), pp. 419--423, 1998.
[37]
M. O'Neill and C. Ryan, "Grammar based function definition in grammatical evolution", Genetic Programming, no. 3, 2000.
[38]
E. Murphy, M. Nicolau, E. Hemberg, M. O'Neill, and A. Brabazon, "Differential gene expression with tree-adjunct grammars", Parallel Problem Solving from Nature-PPSN XII, pp. 377--386, 2012.
[39]
M. Nicolau, M. O'Neill, and A. Brabazon, "Applying genetic regulatory networks to index trading", Parallel Problem Solving from Nature-PPSN XII, pp. 428--437, 2012.

Cited By

View all
  • (2024)Enhancing Program Synthesis with Large Language Models Using Many-Objective Grammar-Guided Genetic ProgrammingAlgorithms10.3390/a1707028717:7(287)Online publication date: 1-Jul-2024
  • (2018)Analysing symbolic regression benchmarks under a meta-learning approachProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208293(1342-1349)Online publication date: 6-Jul-2018

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation
July 2013
1672 pages
ISBN:9781450319638
DOI:10.1145/2463372
  • Editor:
  • Christian Blum,
  • General Chair:
  • Enrique Alba
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: 06 July 2013

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. artificial ant
  2. genetic regulatory network
  3. grammatical evolution
  4. symbolic regression

Qualifiers

  • Research-article

Conference

GECCO '13
Sponsor:
GECCO '13: Genetic and Evolutionary Computation Conference
July 6 - 10, 2013
Amsterdam, The Netherlands

Acceptance Rates

GECCO '13 Paper Acceptance Rate 204 of 570 submissions, 36%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)10
  • Downloads (Last 6 weeks)1
Reflects downloads up to 14 Dec 2024

Other Metrics

Citations

Cited By

View all
  • (2024)Enhancing Program Synthesis with Large Language Models Using Many-Objective Grammar-Guided Genetic ProgrammingAlgorithms10.3390/a1707028717:7(287)Online publication date: 1-Jul-2024
  • (2018)Analysing symbolic regression benchmarks under a meta-learning approachProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208293(1342-1349)Online publication date: 6-Jul-2018

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media