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.Comment: 9 pages, 3 figure
