Gunnar Tufte
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.
- W. R. Ashby. An Introduction to Cybernetics. Chapman & Hall, 1957.Google Scholar
- J. C. Astor and A. C. A developmental model for the evolution of artificial neural networks. Artificial Life, 6(3):189--218, 2000. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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.Google Scholar
- A. W. Burks. Essays On Cellular Automata. University of Illinois Press, 1970.Google Scholar
- E. F. Codd. Cellular Automata. Association for computing machinery, Inc. Monograph series. Academic Press, New York, 1968. Google ScholarDigital Library
- 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.Google Scholar
- D. Federici. Evolving developing spiking neural networks. Evolutionary Computation, 2005. The 2005 IEEE Congress on, 1:543--550 Vol.1, 2-5 Sept. 2005.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- D. Gajski. Principles of Digital Design. Prentice-Hall, 1997. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- T. G. W. Gordon and P. J. Bentley. Bias and scalability in evolutionary development. In GECCO 2005, pages 83 -- 90. ACM Press, 2005. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- P. C. Haddow and G. Tufte. An evolvable hardware FPGA for adaptive hardware. In Congress on Evolutionary Computation(CEC00), pages 553--560. IEEE, 2000.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- H. Kitano. Designing neural networks using genetic algorithms with graph generation systems. Complex Systems, 4(4):461--476, 1990.Google Scholar
- 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. Google ScholarDigital Library
- 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.Google Scholar
- Nallatech. BenERA User Guide, nt107-0072 (issue 3) 09-04-2002 edition, 2002.Google Scholar
- L. Sekanina and M. Bidlo. Evolutionary design of arbitrarily large sorting networks using development. Genetic Programming and Evolvable Machines, 6(3):319--347, 2005. Google ScholarDigital Library
- M. Sipper. Evolution of Parallel Cellular Machines The Cellular Programming Approach. Springer-Verlag, 1997. Google ScholarDigital Library
- M. Sipper. The emergence of cellular computing. Computer, 32(7):18--26, 1999. Google ScholarDigital Library
- W. M. Spears. Gac ga archives source code collection webpage. http://www.aic.nrl.navy.mil/galist/src/, 1991.Google Scholar
- 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. Google ScholarDigital Library
- G. Tufte. Cellular development: A search for functionality. In Congress on Evolutionary Computation(CEC2006), pages 2669--2676. IEEE, 2006.Google Scholar
- 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.Google ScholarCross Ref
- G. Tufte and P. C. Haddow. Towards development on a silicon-based cellular computation machine. Natural Computation, 4(4):387--416, 2005. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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.Google Scholar
- L. Wolpert. Principles of Development, Second edition. Oxford University Press, 2002.Google Scholar
Index Terms
- Phenotypic, developmental and computational resources: scaling in artificial development
Recommendations
Multi-scale computational modeling of developmental biology
Motivation: Normal development of multicellular organisms is regulated by a highly complex process in which a set of precursor cells proliferate, differentiate and move, forming over time a functioning tissue. To handle their complexity, ...
Using feedback to regulate gene expression in a developmental control architecture
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computationWe present what we believe is the first attempt to physically reconstruct the exploratory mechanism of genetic regulatory networks. Feedback plays a crucial role during developmental processes and its mechanisms have recently become much clearer due to ...
Comments