Tackling the IFP Problem with the Preference-Based Genetic Algorithm


In molecular biology, the subject of protein structure prediction is of continued interest, not only to chart the molecular map of living cells, but also to design proteins with new functions. The Inverse Folding Problem (IFP) of finding sequences that fold into a defined structure is in itself an important research problem at the heart of rational protein design. In this work the Preference-Based Genetic Algorithm (PBGA) is employed to find many diversified solutions to the IFP. The PBGA algorithm incorporates a weighted sum model in order to combine fitness and diversity into a single objective function scoring a set of individuals as a whole. By adjusting the sum weights, a direct control of the fitness vs. diversity trade-off in the algorithm population is achieved by means of a selection scheme iteratively removing the least contributing individuals. Experimental results demonstrate the superior performance of the PBGA algorithm compared to other state-of-the-art algorithms both in terms of fitness and diversity.