An annealing approach to associative recall in the CBM model

It is demonstrated how simulated annealing can be used in CBM (classifier-based model) associative retrieval to overcome the problems associated with gradient descent. It is shown that, with its use, the CBM can find a solution as required by the associative recall problem even in extremely disadvantageous object-space topologies where gradient descent fails. Simulated annealing is more robust than conventional gradient descent in reaching a globally optimal solution, as the results are independent of the initial placement of the cue. The use of fixed-temperature finite-length transition chains is shown to yield faster convergence than that of one inhomogeneous temperature chain, as used by H. Szu (1987). A modification of D.S. Johnson´s (1986) formula for determining the initial temperature is likewise found to produce improved results. Appropriate terminating conditions are determined empirically, and solutions are shown to be within an acceptable accuracy level