A new class of hybrid global optimization algorithms for peptide structure prediction: integrated hybrids
A novel class of hybrid global optimization methods for application to the structure prediction in protein-folding problem F is introduced. These optimization methods take the form of a hybrid between a deterministic global optimization algorithm, the alphaBB, and a stochastically based method. conformational space annealing (CSA), and attempt to combine the beneficial features of these two algorithms. The alphaBB method as previously extant exhibits consistency, as it guarantees convergence to the global minimum for twice-continuously differentiable constrained nonlinear programming problems. but can benefit from improvements in the computational front. Computational studies for met-en\kephalin demonstrate the promise for the proposed hybrid global optimization method. (C) 2002 Elsevier Science B.V. All rights reserved.
hybrid global optimization deterministic global optimization protein folding tertiary structure prediction stochastic global optimization