Abstract
A hybrid search algorithm has been developed as an efficient solution for achieving the global optimum of the nonconvex function derived from a Markov random field formulation, which allows for incorporation of complex interactions between the line process variables to better constraint the line process. In the hybrid search, for the stochastic part, an informed genetic algorithm (GA) was developed while employing an incomplete Cholesky preconditioned conjugate gradient algorithm. The GA consists of a reproduction operator and informed mutation operator. The informed mutation operator exploits specific domain knowledge in the search and is accomplished by the Gibbs sampler.