Students will gain a working knowledge of the three primary computational
 intelligence approaches:  simulated annealing, genetic algorithms, and artificial
 neural networks.  The main focus will be on the details of the techniques, 
understanding why the techniques produce accurate results, identifying the 
types of problems each technique is suited for, and determining those problem
 areas for which a technique would be inappropriate.  Students will receive
 "hands-on" experience with experimental computer science via class
 and lab exercises.

I.  Introduction to computational intelligence, 
II.  Overview of simulated annealing (SA),  
III.  SA representation and temperature control,  
IV.  SA problems,  
V.  Overview of genetic algorithms (GA),  
VI.  GA representation and fitness,  
VII.  GA schema theorem and classifiers,  
VIII.  GA problems,  
IX.  Overview of neural networks (NN),  
X.  NN nodes and layers,  
XI.  NN weights and back-propagation,  
XII.  NN problems, 
 XIII.  Other approaches to computational intelligence.