research-article

Phenotypic, developmental and computational resources: scaling in artificial development

Author:

Gunnar TufteAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 859 - 866

https://doi.org/10.1145/1389095.1389261

Published: 12 July 2008 Publication History

Abstract

Developmental systems have inherent properties favourable for scaling. The possibility to generate very large scale structures combined with gene regulation opens for systems where the genome size do not reflect the size and complexity of the phenotype. Despite the presence of scalability in nature there is limited knowledge of what makes a developmental mapping scalable. As such, there is few artificial system that show true scaling. Scaling for any system, biological or artificial, is a question of resources. Toward an understanding of the challenges of scalability the issue of scaling is investigated in an aspect of resources within the developmental model itself. The resources are decompositioned into domains that can be scaled separately each may influence on the outcome of development. Knowledge of the domains influence on scaling provide insight in scaling limitation and what target problems that can be scaled. The resources are decompositioned into three domains; Phenotypic, Developmental and Computational (PDC). The domains are placed along three axes in a PDC-space. To illustrate the principles of scaling in a PDC-space an experimental approach is taken.

References

[1]

W. R. Ashby. An Introduction to Cybernetics. Chapman & Hall, 1957.

[2]

J. C. Astor and A. C. A developmental model for the evolution of artificial neural networks. Artificial Life, 6(3):189--218, 2000.

Digital Library

[3]

V. Beiu, J. M. Yang, L. Quintana, and M. J. Avedillo. Vlsi implementations of threshold logic-a comprehensive survey. IEEE Transactions on Neural Networks, 14(5):1217--1243, September 2003.

Digital Library

[4]

P. J. Bentley and S. Kumar. Three ways to grow designs: A comparison of embryogenies for an evolutionary design problem. In Genetic and Evolutionary Computation Conference (GECCO '99), pages 35--43, 1999.

[5]

A. W. Burks. Essays On Cellular Automata. University of Illinois Press, 1970.

[6]

E. F. Codd. Cellular Automata. Association for computing machinery, Inc. Monograph series. Academic Press, New York, 1968.

Digital Library

[7]

P. Eggenberger Hotz. Asymmetric cell division and its integration with other developmental processes or artificial evolutionary systems. In Artificial Life IX, European Conference on Artificial Life. MIT press, 2004.

[8]

D. Federici. Evolving developing spiking neural networks. Evolutionary Computation, 2005. The 2005 IEEE Congress on, 1:543--550 Vol.1, 2-5 Sept. 2005.

[9]

D. Federici and K. Downing. Evolution and development of a multi-cellular organism: Scalability, resilience and neutral complexification. Artificial Life, 12(3):381--409, 2006.

Digital Library

[10]

D. Gajski. Principles of Digital Design. Prentice-Hall, 1997.

Digital Library

[11]

J. Gauci and K. Stanley. Generating large-scale neural networks through discovering geometric regularities. In GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pages 997--1004, New York, NY, USA, 2007. ACM.

Digital Library

[12]

T. G. W. Gordon. Exploring models of development for evolutionary circuit design. In 2003 Congress on Evolutionary Computation (CEC 2003), pages 2050--2057. IEEE, 2003.

[13]

T. G. W. Gordon and P. J. Bentley. Towards development in evolvable hardware. In the 2002 NASA/DOD Conference on Evolvable Hardware (EH'02), pages 241--250, 2002.

Digital Library

[14]

T. G. W. Gordon and P. J. Bentley. Bias and scalability in evolutionary development. In GECCO 2005, pages 83 -- 90. ACM Press, 2005.

Digital Library

[15]

T. G. W. Gordon and P. J. Bentley. Development brings scalability to hardware evolution. In the 2005 NASA/DOD Conference on Evolvable Hardware (EH'05), pages 272 --279. IEEE, 2005.

Digital Library

[16]

P. C. Haddow and J. Hoye. Achieving a simple development model for 3d shapes: are chemicals necessary? In GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pages 1013--1020, New York, NY, USA, 2007. ACM.

Digital Library

[17]

P. C. Haddow and G. Tufte. An evolvable hardware FPGA for adaptive hardware. In Congress on Evolutionary Computation(CEC00), pages 553--560. IEEE, 2000.

[18]

S. L. Harding, J. F. Miller, and W. Banzhaf. Self-modifying cartesian genetic programming. In GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pages 1021--1028, New York, NY, USA, 2007. ACM.

Digital Library

[19]

M. Kirschner and J. Gerhart. Evolvability. Proceedings of the National Academy of Sciences of the United States of America, 95(15):8420--8427, July 1998.

[20]

H. Kitano. Designing neural networks using genetic algorithms with graph generation systems. Complex Systems, 4(4):461--476, 1990.

[21]

H. Kitano. Building complex systems using development process: An engineering approach. In Evolvable Systems: from Biology to Hardware, ICES, Lecture Notes in Computer Science, pages 218--229. Springer, 1998.

Digital Library

[22]

J. F. Miller. Evolving a self-repairing, self-regulating, french flag organism. In Genetic and Evolutionary Computation (GECCO 2004), Lecture Notes in Computer Science, pages 129--139. Springer, 2004.

[23]

Nallatech. BenERA User Guide, nt107-0072 (issue 3) 09-04-2002 edition, 2002.

[24]

L. Sekanina and M. Bidlo. Evolutionary design of arbitrarily large sorting networks using development. Genetic Programming and Evolvable Machines, 6(3):319--347, 2005.

Digital Library

[25]

M. Sipper. Evolution of Parallel Cellular Machines The Cellular Programming Approach. Springer-Verlag, 1997.

Digital Library

[26]

M. Sipper. The emergence of cellular computing. Computer, 32(7):18--26, 1999.

Digital Library

[27]

W. M. Spears. Gac ga archives source code collection webpage. http://www.aic.nrl.navy.mil/galist/src/, 1991.

[28]

L. Spector and K. Stoffel. Ontogenetic programming. In J. R. Koza, D. E. Goldberg, D. B. Fogel, and R. L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 394--399, Stanford University, CA, USA, 28--31 1996. MIT Press.

Digital Library

[29]

G. Tufte. Cellular development: A search for functionality. In Congress on Evolutionary Computation(CEC2006), pages 2669--2676. IEEE, 2006.

[30]

G. Tufte and P. Haddow. Biologically-inspired: A rule-based self-reconfiguration of a virtex chip. In 4th International Conference on Computational Science 2004 (ICCS 2004), Lecture Notes in Computer Science, pages 1249--1256. Springer, 2004.

[31]

G. Tufte and P. C. Haddow. Towards development on a silicon-based cellular computation machine. Natural Computation, 4(4):387--416, 2005.

Digital Library

[32]

G. Tufte and P. C. Haddow. Extending artificial development: Exploiting environmental information for the achievement of phenotypic plasticity. In 7th International Conference on Evolvable Systems (ICES07), Lecture Notes in Computer Science, pages 297--308. Springer, 2007.

Digital Library

[33]

G. Tufte and J. Thomassen. Size matters: Scaling of organism and genomes for development of emergent structurs. In CODESOAR at Genetic and Evolutionary Computation (GECCO 2006). ACM, 2006.

[34]

L. Wolpert. Principles of Development, Second edition. Oxford University Press, 2002.

Cited By

Lones MFuente LTurner ACaves LStepney SSmith STyrrell A(2014)Artificial Biochemical NetworksIEEE Transactions on Evolutionary Computation10.1109/TEVC.2013.224373218:2(145-166)Online publication date: 1-Apr-2014
https://dl.acm.org/doi/10.1109/TEVC.2013.2243732
Wróbel BAbdelmotaleb AJoachimczak M(2014)Evolving Networks Processing Signals with a Mixed Paradigm, Inspired by Gene Regulatory Networks and Spiking NeuronsBio-Inspired Models of Network, Information, and Computing Systems10.1007/978-3-319-06944-9_10(135-149)Online publication date: 9-Jul-2014
https://doi.org/10.1007/978-3-319-06944-9_10
Haddow PTyrrell A(2011)Challenges of evolvable hardware: past, present and the path to a promising futureGenetic Programming and Evolvable Machines10.1007/s10710-011-9141-612:3(183-215)Online publication date: 19-Jun-2011
https://doi.org/10.1007/s10710-011-9141-6
Show More Cited By

Index Terms

Phenotypic, developmental and computational resources: scaling in artificial development
1. Computing methodologies
  1. Artificial intelligence

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cover image ACM Conferences

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Copyright © 2008 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 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]

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Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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

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Cited By

Lones MFuente LTurner ACaves LStepney SSmith STyrrell A(2014)Artificial Biochemical NetworksIEEE Transactions on Evolutionary Computation10.1109/TEVC.2013.224373218:2(145-166)Online publication date: 1-Apr-2014
https://dl.acm.org/doi/10.1109/TEVC.2013.2243732
Wróbel BAbdelmotaleb AJoachimczak M(2014)Evolving Networks Processing Signals with a Mixed Paradigm, Inspired by Gene Regulatory Networks and Spiking NeuronsBio-Inspired Models of Network, Information, and Computing Systems10.1007/978-3-319-06944-9_10(135-149)Online publication date: 9-Jul-2014
https://doi.org/10.1007/978-3-319-06944-9_10
Haddow PTyrrell A(2011)Challenges of evolvable hardware: past, present and the path to a promising futureGenetic Programming and Evolvable Machines10.1007/s10710-011-9141-612:3(183-215)Online publication date: 19-Jun-2011
https://doi.org/10.1007/s10710-011-9141-6
Tufte G(2009)Metamorphosis and artificial developmentProceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part I10.5555/2022653.2022665(83-90)Online publication date: 13-Sep-2009
https://dl.acm.org/doi/10.5555/2022653.2022665
Haddow PHoye J(2009)Investigating the effect of regulatory decisions in a development modelProceedings of the Eleventh conference on Congress on Evolutionary Computation10.5555/1689599.1689638(293-300)Online publication date: 18-May-2009
https://dl.acm.org/doi/10.5555/1689599.1689638
Haddow PHoye J(2009)Investigating the effect of regulatory decisions in a development model2009 IEEE Congress on Evolutionary Computation10.1109/CEC.2009.4982961(293-300)Online publication date: May-2009
https://doi.org/10.1109/CEC.2009.4982961

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