Bibliography
[1] S. Adra, M. Kiran, P. McMinn, and N. Walkinshaw. A multiobjective optimisation approach for the dynamic inference and refinement
of agent-based model specifications. IEEE Congress on Evolutionary
Computation (CEC), 2011.
[2] S. Adra, S. Tao, S. MacNeil, M. Holcombe, and R. Smallwood. Development of a three dimensional multiscale computational model of the
human epidermis. PLoS One, 5(1), 2010.
[3] A. Al, A. E. Eiben, and D. Vermeulen. An experimental comparison of
tax systems in sugarscape. CIEF, 2000. Online: http://www.cs.vu.
nl/ ~ gusz/papers/CIEF2000-Al-Eiben-Vermeulen.ps.
[4] R. Albert, H. Jeong, and A. L. Barabasi. Error and attack tolerance of
complex networks. Nature, 406(378), 2000.
[5] F. Alkemade. Evolutionary Agent-Based Economics. PhD thesis, Institute of Programming Research and Algorithms, 2004. Part of project
‘Evolutionary Systems for Electronic Markets’.
[6] C. Altavilla, L. Luini, and P. Sbriglia. Information and learning in
Bertrand and Cournot experimental duopolies. Economics Working Paper Series 406, University of Siena, October 2004.
[7] M. Altaweel, N. Collier, T. Howe, R. Najlis, M. North, M. Parker,
E. Tatara, J. R. Vos, L. Girardin, and L. Gulyas. Repast: Recursive Porus Agent Simulation Toolkit, 2005. Online: http://repast.
sourceforge.net/index.html.
[8] P. W. Anderson. More is different: Broken symmetry and the nature of
the hierarchical structure of science. Science, 177:393–396, 1972.
[9] J. Arifovic. Genetic algorithm learning and the cobweb model. Journal
of Economic Dynamics and Control, (18):3–28, 1994.
[10] J. Arifovic. The behavior of exchange rate in the genetic algorithm and
experimental economics. Journal of Political Economy, 104(3):510–541,
1996.
283
[1] S. Adra, M. Kiran, P. McMinn, and N. Walkinshaw. A multiobjective optimisation approach for the dynamic inference and refinement
of agent-based model specifications. IEEE Congress on Evolutionary
Computation (CEC), 2011.
[2] S. Adra, S. Tao, S. MacNeil, M. Holcombe, and R. Smallwood. Development of a three dimensional multiscale computational model of the
human epidermis. PLoS One, 5(1), 2010.
[3] A. Al, A. E. Eiben, and D. Vermeulen. An experimental comparison of
tax systems in sugarscape. CIEF, 2000. Online: http://www.cs.vu.
nl/ ~ gusz/papers/CIEF2000-Al-Eiben-Vermeulen.ps.
[4] R. Albert, H. Jeong, and A. L. Barabasi. Error and attack tolerance of
complex networks. Nature, 406(378), 2000.
[5] F. Alkemade. Evolutionary Agent-Based Economics. PhD thesis, Institute of Programming Research and Algorithms, 2004. Part of project
‘Evolutionary Systems for Electronic Markets’.
[6] C. Altavilla, L. Luini, and P. Sbriglia. Information and learning in
Bertrand and Cournot experimental duopolies. Economics Working Paper Series 406, University of Siena, October 2004.
[7] M. Altaweel, N. Collier, T. Howe, R. Najlis, M. North, M. Parker,
E. Tatara, J. R. Vos, L. Girardin, and L. Gulyas. Repast: Recursive Porus Agent Simulation Toolkit, 2005. Online: http://repast.
sourceforge.net/index.html.
[8] P. W. Anderson. More is different: Broken symmetry and the nature of
the hierarchical structure of science. Science, 177:393–396, 1972.
[9] J. Arifovic. Genetic algorithm learning and the cobweb model. Journal
of Economic Dynamics and Control, (18):3–28, 1994.
[10] J. Arifovic. The behavior of exchange rate in the genetic algorithm and
experimental economics. Journal of Political Economy, 104(3):510–541,
1996.
283