Abstract:
To apply a symbolic learning method to learning in a continuous representation space, the variables spanning the space need to be discretized. When the space is very large, a problem arises as to how to determine a discretization scheme for each variable that is both efficient and effective. This task is particularly important when applying Learnable Evolution Model to optimization problems with very large number of continuous variables. The presented method, called ANCHOR, starts with a low discretization precision of the variables, and then increases the discretization precision in the subranges indicated by the analysis of the descriptions learned using a lower precision. The method has been incorporated in the LEM2 system implementing the Learnable Evolution Model. Experiments with ANCHOR have demonstrated a significant advantage of the method over a fixed discretization method, and enabled LEM2 to optimize functions of large number of continuous variables very effectively.
