A stochastic connectionist approach for global optimization with application to pattern clustering

In this paper, a stochastic connectionist approach is proposed for solving function optimization problems with real-valued parameters. With the assump tion of increased processing capability of a node in the connectionist netw ork, we show how a broader class of problems can be solved. As the proposed approach is a stochastic search technique, it avoids getting stuck in loca l optima. Robustness of the approach is demonstrated on several multi-modal functions with different numbers of variables, Optimization of a well-know n partitional clustering criterion, the squared-error criterion (SEC), is f ormulated as a function optimization problem and is solved using the propos ed approach, This approach is used to cluster selected data sets and the re sults obtained are compared with that of the K-means algorithm and a simula ted annealing (SA) approach. The amenability of the connectionist approach to parallelization enables effective use of parallel hardware.