Evaluating the Success-History Based Adaptive Differential Evolution in the Protein Structure Prediction Problem

Abstract

Proteins are vital macro-molecules for every living organism. As the exper imental determination of protein structures is costly and time-consuming, computational methods became an interesting way to predict proteins’ shape based on their amino acid sequence. Metaheuristics have been employed in the protein structure prediction problem through the years, with different characteristics and different knowledge sources. However, these methods are heavily dependent on parameter tuning, where wrong parameters might cause poor performance. Recently, adaptive strategies were proposed to deal with parameter tuning’s non-trivial task, leaving the algorithm to choose its parameters for each optimization step. Although adaptive metaheuristics are widely applied to benchmark problems, only a few were tested in the PSP problem. To contribute to the analysis of adaptive metaheuristics in the PSP problem, we explore in this work the capability of one of the CEC’14 winners: the Success-History based Adaptive Differential Evolution algorithm on the tertiary protein structure prediction problem. We tested the SHADE algorithm in eight different proteins and compared the algorithm to the other two classical non-adaptive differential evolution and the well-known self-adaptive differential evolution. Moreover, we enhanced the SHADE with domain knowledge from APL. Our results enlarge the research body in adaptive methods for the PSP problem, showing that SHADE is better than non-adaptive differential evolution approaches and competitive compared to self-adaptive differential evolution and related works.