We apply evolutionary algorithms to neural network model of associative memory. In the model, some of the appropriate configurations of the synaptic weights allow the network to store a number of patterns as an associative memory. For example, the so-called Hebbian rule prescribes one such configuration. However, if the number of patterns to be stored exceeds a critical amount (over-loaded), the ability to store patterns collapses more or less. Or, synaptic weights chosen at random do not have such an ability. In this paper, we describe a genetic algorithm which successfully evolves both the random synapses and over-loaded Hebbian synapses to function as associative memory by adoptively pruning some of the synaptic connections. Although many authors have shown that the model is robust against pruning a fraction of synaptic connections, improvement of performance by pruning has not been explored, as far as we know.
|ジャーナル||IEICE Transactions on Information and Systems|
|出版ステータス||出版済み - 1998|
All Science Journal Classification (ASJC) codes
- コンピュータ ビジョンおよびパターン認識