This work presents a new evolutionary algorithm based on the standard harmony search strategy, called population-based harmony search (PBHS). Also, this work provides a parallelisation method for the proposed PBHS by using graphical processing units (GPU), allowing multiple function evaluations at the same time. Experiments were done using a benchmark of a hard scientific problem: protein structure prediction with the AB-2D off-lattice model. The performance and the solution quality were evaluated and compared using four implementations: two concerning the standard HS, one running in CPU and another running in GPU, and two implementations concerning the PBHS, also running in CPU and in GPU. Results show that the quality of solutions and speed-ups achieved by the PBHS is significantly better than the HS.

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