Abstract
Combinatorial optimization by means of an analog neural network proposed by J. J. Hopfield and D. W. Tank is one of the major subjects in neural computing. Though a good deal of reexaminations and various applications of the network have been reported so far, the effectiveness of the approach is not clarified in sufficient manner. In the present paper, the effectiveness of the Hopfield model is discussed through its application to a circuit block placement problem. The results of computer simulation show that, although the Hopfield model is not effective enough if it is used without sophisticated preexamination of combinatorial problems, it has ability to yield quite satisfactory solutions when it is endowed with an appropriate form and parameters of the energy function. The meaning of appropriate parameter values yielding good solutions is also investigated theoretically.
