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For easy reference, Table ![[*]](http://sepwww.stanford.edu/latex2html/cross_ref_motif.gif)
shows the evolution parameters
selected as optimum from the previous tests. The resulting genetic algorithm
will be compared with a micro-genetic algorithm described in the next section.
Table:
Summary of the optimum evolution parameters for the standard
genetic algorithm
| Population size |
200 |
Crossover rate |
0.6 |
| Mutation rate |
0.002 |
Creep mutation rate |
0.02 |
| Elitism |
Yes |
Niching |
No |
| Selection strategy |
Tournament |
Number of children |
1 |
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Up: Standard Genetic Algorithm
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Stanford Exploration Project
11/11/2002