Laura M. Grabowski
ABSTRACT
We present our first results concerning the de novo evolution of motility and tactic response in systems of digital organisms. Our model organism was E. coli and the behavior of interest was gradient following, since this represents simple decision-making. Our first experiments demonstrated the evolution of a tactic response, both when provided with a hand-coded system to remember previous gradient concentrations and without this crutch where the organisms must determine how to store previous values on their own. In our second set of experiments we investigated two different rotation strategies, random and systematic, and found no significant performance difference between the two strategies. These experiments served as a stepping-stone and proof-of-concept of the infrastructure needed for our future work on the evolution of simple intelligence.
- C. Adami, C. A. Ofria, and T. C. Collier. Evolution of biological complexity. Proceedings of the Natlional Academy of Science, 97:4463--4468, 2000.Google Scholar
Cross Ref
- J. Adler. Chemotaxis in bacteria. Science, 153(3737):708--716, August 12 1966.Google ScholarCross Ref
- J. Adler. Chemotaxis in bacteria. Annual Review of Biochemistry, 44:341--356, 1975.Google ScholarCross Ref
- J. Adler. Primitive sensory and communication systems: the taxes and tropisms of micro-organisms and cells, chapter Chemotaxis in bacteria, pages 91--100. Academic Press, London, 1975.Google Scholar
- J. Ayers, J. Witting, N. McGruer, C. Olcott, and D. Massa. Lobster robots. In T. Wu and K. N, editors, Proceedings of the International Symposium on Aqua Biomechanisms, Tokai University, 2000.Google Scholar
- R. D. Beer and J. C. Gallagher. Evolving dynamical neural networks for adaptive behavior. Adaptive Behavior, 1(1):91--122, 1992. Google ScholarDigital Library
- H. C. Berg. E. coli in motion. Springer, New York, 2004.Google ScholarCross Ref
- H. C. Berg and D. A. Brown. Chemotaxis in \textitEscherichia coli analysed by three-dimensional tracking. Nature, 239:500--504, 27 October 1972.Google ScholarCross Ref
- V. Braitenberg. Vehicles: experiments in synthetic psychology. MIT Press, Cambridge, MA, 1984.Google Scholar
- K. L. Briggman, H. D. I. Arbanel, and W. B. Kristan Jr. Optical imaging of neural populations during decision-making. Science, 307(5711):896--901, 2005.Google ScholarCross Ref
- M. J. Carlile. Primitive sensory and communication systems: the taxes and tropisms of micro-organisms and cells, chapter Taxes and tropisms: diversity, biological significance and evolution, pages 1--28. Academic Press, London, 1975.Google Scholar
- A. L. Christensen and M. Dorigo. Evolving an integrated phototaxis and hole-avoidance behavior for a swarm-bot. In L. M. Rocha, L. S. Yaeger, M. A. Bedau, D. Floreano, R. L. Goldstone, and A. Vespignani, editors, Proceedings of the 10th International Conference on the Simulation and Synthesis of Living Systems (Alife X), pages 248--254. MIT Press, Cambridge, MA, 2006.Google Scholar
- J. D. E. Koshland. A model regulatory system: bacterial chemotaxis. Physiological Reviews, 59(4):811--862, October 1979.Google ScholarCross Ref
- D. C. Dennett. The new replicators. In M. Pagel, editor, Encyclopedia of Evolution, pages E83--E92. Oxford University Press, New York, 2002.Google Scholar
- A. Dhariwal, G. Sukhatme, and A. A. Requicha. Bacterium-inspired robots for environmental monitoring. In Proceedings of 2004 IEEE/RSJ International Conference on Robotics and Automation, pages 1436--1443, New Orleans, LA, April 2004.Google ScholarCross Ref
- M. Eyiyurekli, P. I. Lelkes, and D. E. Breen. A computational system for investigating chemotaxis-based cell aggregation. In F. A. e Costa, L. M. Rocha, E. Costa, I. Harvey, and A. Coutinho, editors, Advances in Artificial Life, 9th European Conference, ECAL 2007, Lisbon, Portugal, September 10-14, 2007, Proceedings ECAL, number 4648 in Lecture Notes in Computer Science, pages 1034--1049. Springer, 2007. Google ScholarDigital Library
- D. Floreano and F. Mondada. Evolutionary neurocontrollers for autonomous mobile robots. Neural Networks, 11:1461--1478, 1998. Google ScholarDigital Library
- F. W. Grasso, T. R. Consi, D. C. Mountain, and J. Atema. Biomimetic lobster performs chemo-orientation in turbulence using a pair of spatially separated sensors: progress and challenges. Robotics and Autonomous Systems, 30:115--131, 2000.Google ScholarCross Ref
- J. Kodjabachian and J.-A. Meyer. Evolution and development of neural controllers for locomotion, gradient-following, and obstacle-avoidance in artificial insects. IEEE Transactions on Neural Networks, 9(5):796--812, 1998. Google ScholarDigital Library
- R. E. Lenski, C. Ofria, R. T. Pennock, and C. Adami. The evolutionary origin of complex features. Nature, 423:139--144, 2003.Google ScholarCross Ref
- T. M. Morse, S. R. Lockery, and T. C. Ferrée. Robust spatial navigation in a robot inspired by chemotaxis in \textitCaenorhabditis elegans. Adaptive Behavior, 6:393--410, 1998. Google ScholarDigital Library
- C. Ofria, C. Adami, and T. C. Collier. Design of evolvable computer languages. IEEE Transactions in Evolutionary Computation, 17:528--532, 2002. Google ScholarDigital Library
- C. Ofria and C. O. Wilke. Avida: a software platform for research in computational evolutionary biology. In Artificial Life 10, pages 191--229, 1994. Google ScholarDigital Library
- M. D. Onsum and A. P. Arkin. Autonomous mobile robot control based on white blood cell chemotaxis. Lecture Notes in Computer Science, 3082:9--19, 2005. Google ScholarDigital Library
- G. W. Ordal. Bacterial chemotaxis: a primitive sensory system. BioScience, 30(6):408--411, 1980.Google ScholarCross Ref
- T. Sharpe and B. Webb. Simulated and situated models of chemical trail following in ants. In R. Pfeifer, B. Blumberg, J.-A. Meyer, and S. W. Wilson, editors, From animals to animats 5: proceedings of the fifth international conference on simulation of adaptive behavior, pages 195--204, Cambridge, MA, 1998. MIT Press. Google ScholarDigital Library
- H. A. Simon. Models of man--social and rational. John Wiley and Sons, New York, 1957.Google Scholar
- O. S. Soyer, T. Pfeiffer, and S. Bonhoeffer. Simulating the evolution of signal transduction pathways. Journal of Theoretical Biology, 241:223--232, 2006.Google ScholarCross Ref
- R. A. Watson, S. G. Ficici, and J. B. Pollack. Embodied evolution: embodying an evolutionary algorithm in a population of robots. In P. J. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao, and A. Zalzala, editors, Proceedings of the Congress on Evolutionary Computation, volume 1, pages 335--342, Mayflower Hotel, Washington D.C., USA, 6-9 1999. IEEE Press.Google Scholar
- B. Webb. Robotic experiments in cricket phonotaxis. In D. Cliff, P. Husbands, J. Meyer, and S. Wilson, editors, From animals to animats 3: proceedings of the fifth international conference on simulation of adaptive behavior, Cambridge, MA, 1994. MIT Press. Google ScholarDigital Library
- B. Webb. Using robots to model animals: a cricket test. Robotics and Autonomous Systems, 16:117--134, 1995.Google ScholarCross Ref
- B. Webb. Robots, crickets and ants: models of neural control of chemotaxis and phonotaxis. Neural Networks, 11:1479--1496, 1998. Google ScholarDigital Library
Index Terms
- On the evolution of motility and intelligent tactic response
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