GENETIC LANDER : AN EXPERIMENT
IN ACCURATE NEURO-GENETIC CONTROL

Edmund Ronald and Marc Schoenauer


The control problem of soft-landing a toy lunar module simulation
is investigated in the context of neural nets. While
traditional supervised back-propagation training is inappropriate
for lack of training exemplars, genetic algorithms allow
a controller to be evolved without difficulty:  Evolution is a 
form of unsupervised learning.
A novelty introduced in this paper is the presentation
of additional renormalized inputs to the net; experiments 
indicate that the presence of such inputs allows precision 
of control to be attained faster, when learning time is 
measured by the number of generations for which the GA 
must run to attain a certain mean performance.


Keywords : Genetic Algorithms, Control and Trajectory Planning, 
Neural Networks, Fine Tuning.

The whole paper is available as a compressed Postscript file by internet
procedure FTP anonymous on host cmapx.polytechnique.fr ( 129.104.4.100)
in the directory pub/RI/1994 under the name

         ronald_schoenauer_299.avr.ps.gz