Hybrid genetic algorithms for minimization of a polypeptide specific energy model

A hybrid genetic algorithm for polypeptide structure prediction is proposed which incorporates efficient gradient-based minimization directly in the fitness evaluation. Fitness is based on a polypeptide specific potential energy model. The algorithm includes a replacement frequency parameter which specifies the probability with which an individual is replaced by its minimized counterpart. Thus, the algorithm can implement either Baldwinian, Lamarckian, or probabilistically Lamarckian evolution. Experiments are described which compare the effectiveness of the genetic algorithm with and without the local minimization operator, and for various probabilities of replacement. The experiments apply the techniques to the minimization of the ECEPP/2 energy model for [Met] Enkephalin. Using fitness proportionate selection, the hybrid approaches obtain better energies (and better basins of attraction) than the standard genetic algorithm, and often find the global minimum. When tournament selection is used, the results are qualitatively similar, except that the hybrid approaches are prone to premature convergence.