HARMONIC COMPETITION - A SELF-ORGANIZING MULTIPLE CRITERIA OPTIMIZATION

Harmonic competition is a learning strategy based upon winner-take-all or winner-take-quota with respect to a composite of heterogeneous sub costs. This learning is unsupervised and organizes itself, The subcost s may conflict with each other, Thus, the total learning system realiz es a self-organizing multiple criteria optimization, The subcosts are combined additively and multiplicatively using adjusting parameters, F or such a total cost, a general successive learning algorithm is deriv ed first, Then, specific problems in the Euclidian space are addressed , Vector quantization with various constraints and traveling salespers on problems are selected as test problems, The former is a typical cla ss of problems where the number of neurons is less than that of the da ta, The latter is an opposite case, Duality exists in these two classe s, In both cases? the combination parameters of the subcosts show wide dynamic ranges in the course of learning, It is possible, however, to decide the parameter control from the structure of the total cost, Th is method finds a preferred solution from the Pareto optimal set of th e multiple object optimization, Controlled mutations motivated by gene tic algorithms are proved to be effective in finding near-optimal solu tions, All results show significance of the additional constraints and the effectiveness of the dynamic parameter control.