A Class of Pareto Archived Evolution Strategy Algorithms Using Immune Inspired Operators for Ab-Initio Protein Structure Prediction

摘要

In this work we investigate the applicability of a multiobjec-tive formulation of the Ab-Initio Protein Structure Prediction (PSP) to medium size protein sequences (46-70 residues). In particular, we introduce a modified version of Pareto Archived Evolution Strategy (PAES) which makes use of immune inspired computing principles and which we will denote by "I-PAES". Experimental results on the test bed of five proteins from PDB show that PAES, (1+1)-PAES and its modified version I-PAES, are optimal multiobjective optimization algorithms and the introduced mutation operators, mut_1 and mut_2, are effective for the PSP problem. The proposed I-PAES is comparable with other evolutionary algorithms proposed in literature, both in terms of best solution found and computational cost.