| Automatic Problem Solver 3.0 uses two different learning algorithms for
Classification problems. The first is the most efficient in evolutionary terms as it uses simpler chromosomal architectures and therefore can discover very good models in record time. Fortunately enough, this algorithm is also the most efficient in terms of the quality of the models created. For obvious reasons, this algorithm – the
basic gene expression algorithm – is the default in APS 3.0. The chromosomal architecture of the basic gene expression algorithm does not support the direct manipulation of random numerical constants and, therefore, it can only create numerical constants from scratch or invent new ways of representing them. The second learning algorithm of APS 3.0 explores a different chromosomal architecture that allows the direct manipulation of random numerical constants and, therefore, can be used to design complex models with more conventional tools. You activate this algorithm in the Settings Panel -> Numerical Constants by checking the Use Random Numerical Constants box.
As mentioned above, the second learning algorithm – gene
expression programming with random numerical constants or GEP-RNC
for short – is slightly more complex than GEP
as it uses an additional gene domain (Dc) for encoding the random
numerical constants. Consequently, this algorithm comes equipped
with an additional set of genetic operators (RNC
mutation, Dc mutation,
Dc inversion, and Dc
IS transposition) especially developed for handling random
numerical constants (if you are not familiar with these operators,
please use the default values by clicking the Defaults button for
they work very well in all cases). |
