A. Gallini
ABSTRACT
Evolutionary computation has been often used by computer scientists to evolve the morphologies and control systems of artificial life. Artificial 'brains', behaviour strategies, methods of communication, distributed problem solving and many other topics are commonly explored by using genetic algorithms and other evolutionary search techniques. We think that this approach may provide the general guidelines to efficiently manage and "design" computation on large and homogeneous lattices of simple, asynchronously interacting processing elements. Because of their structural simplicity, this kind of substrates will be suitable architectural models for computational machines based on molecular scale devices. In this paper we present an environment named Bio-molecular Engine (BME), in which different substrates can be simulated and used as "artificial worlds" where computational entities can rise, grow and evolve. In particular we discuss how to use a grid to evolutionary find a good solution to a well defined design issue: how much parallelism is good for a given problem computed in our environment.
- Lisa J K Durbeck, Nicholas J Macias. The Cell Matrix: an architecture for nanocomputing. Nanotechnology, no. 12, pp. 217--230, Sep. 2001.Google Scholar
Cross Ref
- Paul Beckett, and Andrew Jennings. Towards nanocomputer architecture. In Australian Computer Science Communications, Proceedings of the seventh Asia-Pacific conference on Computer systems architecture, Vol.6, Jan. 2002. Google ScholarDigital Library
- Paul Beckett. VLSI in the nanometer era: Exploiting multiple functionality for nano-scale reconfigurable systems. In Proceedings of the 13th ACM Great Lakes symposium on VLSI, Apr. 2003. Google ScholarDigital Library
- Ferdinand Peper, Member, Jia Lee, Fukutaro Abo, Teijiro Isokawa, Member, Nobuyuki Matsui and Shinro Mashiko. Fault-Tolerance in Nanocomputers: A Cellular Array Approach. IEEE Trans. on nanotechnology, vol. 3, no. 1, pp. 187--201, March 2004. Google ScholarDigital Library
- Walid A. Najjar, Wim Böhm Bruce A. Draper, Jeff Hammes Robert Rinker, J. Ross Beveridge, Monica Chawathe and Charles Ross. High-Level Language Abstraction for Reconfigurable Computing. IEEE Computer - Innovative Technology for Computing professionals, pp. 63--69, Aug. 2003. Google ScholarDigital Library
- J. P. Hammes, R. E. Rinker, D. M. McClure, A. P. W. Böhm, W. A. Najjar. The SA-C Compiler Dataflow Descritpion. ACM Transactions on Embedded Computing Systems (TECS) Vol. 2, Issue 4, pp. 560--589, Nov. 2003. Google ScholarDigital Library
- Mihai Budiu, Girish Venkataramani, Tiberiu Chelcea and Seth Copen Goldstein. Spatial Computation. In Eleventh International Conference on Architectural Support for Programming Languages and Operating Systems, Oct. 2004, Boston, Massachusetts, USA. Google ScholarDigital Library
- Girish Venkataramani, Mihai Budiu, Tiberiu Chelcea and Seth Copen Goldstein. C to Asynchronous Dataflow Circuits: An End-to-End Toolflow. In Thirteenth International Workshop on Logic and Synthesis, June 2004, Temecula, California, USA.Google Scholar
- Seth Copen Goldstein and Dan Rosewater. What Makes a Good Molecular Scale Computer Device?. Technical Report CMU-CS-02-181, September 2002.Google ScholarCross Ref
- Sven Bodo Scholz. Single Assignment C -- Functional Programming Using Imperative Style. In Functional Languages Implementation Workshop. Paper 21.Google Scholar
- Luca Benini, Giovanni De Micheli. Network On Chip: A New SoC Paradigm. In Design, Automation and Test in Europe Conference and Exhibition, Paris, France, March 2002.Google Scholar
- Girish V. Varatkar, Radu Marculescu. On-Chip Traffic Modeling and Synthesis for MPEG-2 Video Applications. IEEE Trans. on Very Large Scale Integration systems, vol. 12, no. 1, pp. 108--119, Jan. 2004. Google ScholarDigital Library
- C. Teuscher, D. Mange, A. Stauer, and G. Tempesti. Bio-Inspired Computing Tissues: Towards Machines that Evolve, Grow, and Learn. In Fourth International Workshop on Information Processing in Cells and Tissues, Inter-University Micro Electronics Center Kapeldreef 75 3001 Heverlee (Leuven) Belgium, Apr. 24, 2001.Google Scholar
- Gianluca Tempesti, Daniel Roggen, Eduardo Sanchez, Yann Thoma, Richard Canham and Andy Tyrrell. Ontogenetic Development and Fault Tolerance in the POEtic Tissue. In The 5th International Conference on Evolvable Systems: From Biology to Hardware, March 2003. Google ScholarDigital Library
- Yann Thoma, Eduardo Sanchez, Juan-Manuel Moreno Arostegui and Gianluca Tempesti. A Dynamic Routing Algorithm for a Bio-Inspired Reconfigurable Circuit. Field-Programmable Logic and Applications pp. 681--690, 2003.Google Scholar
- Andrè DeHon. Compact, Multilayer Layout for Butterfly FatTree. In Twelfth Annual ACM Symposium on Parallel Algorithms and Architectures, pp. 206--215, July 9--12, 2000. Google ScholarDigital Library
- C. S. Lent and P. D. Tougaw. A device architecture for computing with quantum dots. Proc. IEEE, vol. 85, pp. 541--557, 1997.Google ScholarCross Ref
- Craig S. Lent, P. Douglas Tougaw, Wolfgang Porod, and Gary H. Bernstein. Quantum Cellular Automata. Nanotechnology 4, pp. 49--57, 1993.Google Scholar
- P. Douglas Tougaw and Craig S. Lent. Dynamic behavior of quantum cellular automata. Journal of Applied Physics, Vol. 80, pp. 4722--4736 Oct. 15, 1996.Google ScholarCross Ref
- Konrad Walus, Timothy J. Dysart, Graham A. Jullien and R. Arief Budiman. QCA Designer: A Rapid Design and Simulation Tool for Quantum-Dot Cellular Automata. IEEE Trans. on nanotechnology, Vol. 3, no. 1, March 2004. Google ScholarDigital Library
- C. Ghezzi, Mehdi Jazayeri, Programming Language Concepts. Third Edition, Wiley 1998. Google ScholarDigital Library
- Robert P. Wilson, Robert S. French, Christopher S. Wilson, Saman P. Amarasinghe, Jennifer M. Anderson, Steve W. K. Tjiang, Shih-Wei Liao, Chau-Wen Tseng, Mary W. Hall, Monica S. Lam, and John L. Hennessy. SUIF: An infrastructure for research on parallelizing and optimizing compilers. In ACM SIGPLAN Notices, Vol. 29, pp. 31--37, Dec. 1994. Google ScholarDigital Library
- John R. Koza. Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems. Technical Report: CS-TR-90-1314, June 1990. Google ScholarDigital Library
- John R. Koza and James P. Rice. Genetic Generation of Both the Weights and Architecture for a Neural Network. In International Joint Conference on Neural Networks, Vol. 2 pp. 307--404, Seattle, Washington, USA, 1991.Google Scholar
- Melanie Mitchell. An introduction to genetic algorithms. MIT Press, 1996. Google ScholarDigital Library
- Peter Bentley. Aspects of Evolutionary Design by Computers. In Proceedings of the 3rd On-line World Conference on Soft Computing in Engineering Design and Manufacturing, 1998.Google Scholar
Index Terms
- Bio Molecular Engine: a bio-inspired environment for models of growing and evolvable computation
Recommendations
Use of the q-Gaussian mutation in evolutionary algorithms
Special issue on advances in computational intelligence and bioinformaticsThis paper proposes the use of the q-Gaussian mutation with self-adaptation of the shape of the mutation distribution in evolutionary algorithms. The shape of the q-Gaussian mutation distribution is controlled by a real parameter q. In the proposed ...
Improving the scalability of EA techniques: a case study in clustering
EA'09: Proceedings of the 9th international conference on Artificial evolutionThis paper studies how evolutionary algorithms (EA) scale with growing genome size, when used for similarity-based clustering. A simple EA and EAs with problem-dependent knowledge are experimentally evaluated for clustering up to 100,000 objects. We ...
Variations of biogeography-based optimization and Markov analysis
Biogeography-based optimization (BBO) is a new evolutionary algorithm that is inspired by biogeography. Previous work has shown that BBO is a competitive optimization algorithm, and it demonstrates good performance on various benchmark functions and ...
Comments