  Nuclear Magnetic Resonance (NMR) Spectroscopy is a widely used technique to
predict the native structure of proteins. However, NMR machines are only able
to report approximate and partial distances between pair of atoms. To build the
protein structure one has to solve the Euclidean distance geometry problem
given the incomplete interval distance data produced by NMR machines. In this
paper, we propose a new genetic algorithm for solving the Euclidean distance
geometry problem for protein structure prediction given sparse NMR data. Our
genetic algorithm uses a greedy mutation operator to intensify the search, a
twin removal technique for diversification in the population and a random
restart method to recover stagnation. On a standard set of benchmark dataset,
our algorithm significantly outperforms standard genetic algorithms.
