Early evolution of memory usage in digital organisms

Artificial Life XII: Proceedings of the 12th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2010

Volumn , Issue , 2010, Pages 224-231

  Grabowski, Laura M ^a   Bryson, David M ^b   Dyer, Fred C ^b   Ofria, Charles ^b   Pennock, Robert T ^b  

^a UNIVERSITY OF TEXAS PAN AMERICAN   (United States)

^b MICHIGAN STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ENVIRONMENTAL CUES; EXPERIMENTAL ENVIRONMENT; FLEXIBLE BEHAVIORS; MEMORY USAGE; NATURAL SELECTION; OPTIMAL DECISION MAKING; PRIOR EXPERIENCE;

ARTIFICIAL INTELLIGENCE; CHEMISTRY; CIRCUIT SIMULATION; GENETIC ENGINEERING; SOFTWARE ENGINEERING; SPEECH SYNTHESIS;

BIOLOGICAL SYSTEMS;

EID: 84874670977     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. Adami, C., Ofria, C. A., and Collier, T. C. (2000). Evolution of biological complexity. Proceedings of the National Academy of Science, 97: 4463-4468.
2. Blynel, J. and Floreano, D. (2003). Exploring the T-maze: evolving learning-like robot behaviors using CTRNNs. In Cagnoni, S., editor, Applications of Evolutionary Computing, Lecture Notes in Computer Science, pages 593-604. Springer, Berlin.
3. Collett, T. S. and Baron, J. (1995). Learnt sensori-motor mappings in honeybees: interpolation and its possible relevance to navigation. Journal of Comparative Physiology A, 177: 287-298.
4. Collett, T. S., Fry, S. N., and Wehner, R. (1993). Sequence learning by honeybees. Journal of Comparative Physiology A, 172: 693-706.
5. Collett, T. S., Graham, P., and Durier, V. (2003). Route learning by insects. Current Opinion in Neurobiology, 13: 718-725.
6. Dukas, R. (2008). Evolutionary biology of insect learning. Annual Review: Entomology, 53: 145-160.
7. Dukas, R. and Bernays, E. A. (2000). Learning improves growth rate in grasshoppers. Proceedings of the National Academy of Science, 97(6): 2637-2640.
8. Floreano, D. and Urzelai, J. (2000). Evolutionary robots with online self-organization and behavioral fitness. Neural Networks, 13: 431-443.
9. Graham, P., Fauria, K., and Collett, T. S. (2003). The influence of beacon-aiming on the routes of wood ants. Journal of Experimental Biology, 206: 535-541.
10. Hartmann, G. and Wehner, R. (1995). The ant's path integration system: a neural architecture. Biological Cybernetics, 73: 483-497.
11. Lenski, R. E., Ofria, C., Pennock, R. T., and Adami, C. (2003). The evolutionary origin of complex features. Nature, 423:139-144.
12. Mittelstaedt, H. (1985). Analytical cybernetics of spider navigation. In Barth, F. G., editor, Neurobiology of arachnids, pages 298-316. Springer, Berlin.
13. Müller, M. and Wehner, R. (1988). Path integration in desert ants, Cataglyphis fortis. Proceedings of the National Academy of Sciences of the United States of America, 85: 5287-5290.
14. Nolfi, S. and Floreano, D. (2002). Synthesis of autonomous robots through evolution. Trends in Cognitive Sciences, 6(1): 31-37.
15. Nolfi, S., Miglino, O., and Parisi, D. (1994). Phenotypic plasticity in evolving neural networks. In Proceedings of the PerAc '94 Conference, pages 146-157, Los Alamitos, CA. IEEE, IEEE Computer Society Press.
16. Ofria, C., Adami, C., and Collier, T. C. (2002). Design of evolvable computer languages. IEEE Transactions in Evolutionary Computation, 17: 528-532.
17. Ofria, C. and Wilke, C. O. (2004). Avida: a software platform for research in computational evolutionary biology. Artificial Life, 10(2): 191-229.
18. Pennock, R. T. (2007). Models, simulations, instantiations, and evidence: the case of digital evolution. Journal of Experimental and Theoretical Artificial Intelligence, 19(1): 29-42.
19. Rescorla, R. A. (1988). Behavioral studies of Pavlovian conditioning. Annual Review of Neuroscience, 11: 329-352.
20. Shettleworth, S. J. (1998). Cognition, evolution, and behavior. Oxford University Press, New York.
21. Todd, P. M. and Miller, G. F. (1990). Exploring adaptive agency II: simulating the evolution of associative learning. In From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior on, pages 306-315. MIT Press, Cambridge, MA, USA.
22. Vickerstaff, R. and Di Paolo, E. A. (2005). Evolving neural models of path integration. Journal of Experimental Biology, 208: 3349-3366.
23. Zhang, S. W., Bartsch, K., and Srinivasan, M. V. (1996). Maze learning by honeybees. Neurobiology of Learning and Memory, 66: 267-282.
24. Zhang, S. W., Lehrer, M., and Srinivasan, M. V. (1999). Honeybee memory: navigation by associative grouping and recall of visual stimuli. Neurobiology of Learning and Memory, 72: 180-201.
25. Zhang, S. W., Mizutani, A., and Srinivasan, M. V. (2000). Maze navigation by honeybees: learning path regularity. Learning and Memory, 7: 363-374.