Physics – Condensed Matter – Disordered Systems and Neural Networks
Scientific paper
1997-04-17
Physics
Condensed Matter
Disordered Systems and Neural Networks
9 pages, 3 figures
Scientific paper
10.1209/epl/i1998-00410-4
We propose a general learning algorithm for solving optimization problems, based on a simple strategy of trial and adaptation. The algorithm maintains a probability distribution of possible solutions (configurations), which is updated continuously in the learning process. As the probability distribution evolves, better and better solutions are shown to emerge. The performance of the algorithm is illustrated by the application to the problem of finding the ground state of the Ising spin glass. A simple theoretical understanding of the algorithm is also presented.
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