ABSTRACT
No abstract available.
- S. Bauer, B. Nolet, J. Giske, J. Chapman, S. Akesson, A. Hedenstrom, and J. Fryxell. Cues and decision rules in animal migration. In Animal Migration: A Synthesis, pages 69--87. Oxford University Press, Oxford, UK, 2011.Google ScholarCross Ref
- E. Bonabeau, M. Dorigo, and G. Theraulaz. Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, Oxford, England, 1998. Google ScholarDigital Library
- D. Curran and C. O'Riordan. The effects of cultural learning in populations of neural networks. Artificial Life, 13(1):45--67, 2007. Google ScholarDigital Library
- S. Edwards. The Chaos of Forced Migration: A Means of Modeling Complexity for Humanitarian Ends. Oxford University Press, Oxford, United Kingdom, 2009.Google Scholar
- A. Eiben and J. Smith. Introduction to Evolutionary Computing. Springer-Verlag, Berlin, Germany, 2003. Google ScholarCross Ref
- A. Engelbrecht. Computational Intelligence An Introduction. Second Edition. John Wiley and Sons, Hoboken, USA, 2007. Google ScholarDigital Library
- B. Flannery, S. Teukolsky, and W. Vetterling. Numerical Recipes. Cambridge University Press, Cambridge, 1986.Google Scholar
- F. Gomez and R. Miikkulainen. Incremental evolution of complex general behavior. Adaptive Behavior, 5(1):317--342, 1997. Google ScholarDigital Library
- C. Hemelrijk. The use of artificial-life models for the study of social organization. In Macaque Societies: A Model for the Study of Social Organization, pages 295--313. Cambridge University Press, Cambridge, UK, 2004.Google Scholar
- J. Kennedy and R. Mendes. Population structure and particle swarm performance. In Proceedings of the 2002 Congress on Evolutionary Computation, pages 1671--1676, Honolulu, USA, 2002. IEEE Press. Google ScholarDigital Library
- L. Langenhoven and G. Nitschke. Neuro-evolution versus particle swarm optimization for competitive co-evolution of pursuit-evasion behaviors. In Proceedings of the IEEE Congress on Evolutionary Computation. IEEE Press, 2010.Google ScholarCross Ref
- U. Lopez, J. Gautrais, I. Couzin, and G. Theraulaz. From behavioural analyses to models of collective motion in fish schools. Interface Focus, 2(2):693--707, 2012.Google ScholarCross Ref
- R. Reynolds. An introduction to cultural algorithms. In Proceedings of the Third Annual Conference on Evolutionary Programming, pages 131--139, Singapore, 1994. World Scientifc.Google Scholar
- K. Stanley and R. Miikkulainen. A taxonomy for artificial embryogeny. Artificial Life, 9(2):93--130, 2003. Google ScholarDigital Library
- B. Sumida, A. Houston, J. McNamara, and W. Hamilton. Genetic algorithms and evolution. Journal of Theoretical Biology, 147(1):59--84, 1990.Google ScholarCross Ref
Index Terms
- Lifetimes of migration
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