next up previous
Next: About this document ... Up: The Use of Genetic Previous: Conclusions

Bibliography

1
ACKLEY, D., AND LITMAN, M.
Interactions between learning and evolution.
In Proceedings of the Second Conference on Artificial Life (Reading, MA, 1991), Addison-Wesley, pp. 487-509.

2
BALDWIN, J.
Development and Evolution: Including Psychological Evolution, Evolution by Orthoplasy and Theory of Genetics.
AMS Press, New York, NY, 1990.

3
FOGEL, D.
Evolutionary Computation.
IEEE Press, New York, NY, 1995.

4
HAGEN, M., DEMUTH, H., AND BEALE, M.
Neural Network Design.
PWS, Boston, MA, 1995.

5
HINTON, G. E., AND NOLWAN, S. J.
How learning can guide evolution.
Complex Systems 1 (1987), 495-502.

6
KOHONEN, T.
Self-Organizing Maps.
Springer-Verlag, Berlin, Germany, 1995.

7
LEVY, S.
Artificial Life: A Quest for New Creation.
Pantheon Books, New York, NY, 1992.

8
MITCHELL, M.
Introduction to Genetic Algorithms.
MIT Press, Cambridge, MA, 1996.

9
RAO, V., AND RAO, H.
C++ Neural Networks and Fuzzy Logic.
IDG Books, Foster City, CA, 1995.

10
SANTAMARIA, J., AND SUTTON, R.
A standard interface for reinforcement learning, 1996.
[http://envy.cs.umass.edu/$\sim$rich/RLinterface
/RLinterface.html].

11
WALL, M.
Galib, a collection of genetic programming components, 1998.
[http://lancet.mit.edu].

12
WHITLEY, D., GORDON, V. S., AND MATHIAS, K.
Lamarckian evolution, the baldwin effect and function optimization.
In Parallel Problem Solving from Nature-PPSN III. (1994), Y. Davidor, H. Schwefel, and R. Manner, Eds., no. 866 in Lecture Notes in Computer Science, pp. 6-15.


Aaron Konstam
1999-10-04