Automatic Problem Solver 3.0 uses two different learning algorithms for Function Finding 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).
And last but not least since these parameters are crucial if you are handling numerical constants directly, you must also choose and adjust the range and type of numerical constants that will be used by the
GEP-RNC algorithm during the learning process. As for the
Number of Constants per Gene parameter, a good rule of thumb consists of using a small set of 10 different constants per gene as this seems to provide enough diversity for most problems without inflating the structural complexity much.
|