A class of pareto archived evolution strategy algorithms using immune inspired operators for ab-initio protein structure prediction

Authors:
Vincenzo Cutello;Giuseppe Narzisi;Giuseppe Nicosia
Affiliations:
Department of Mathematics and Computer Science, University of Catania, Catania, Italy;Department of Mathematics and Computer Science, University of Catania, Catania, Italy;Department of Mathematics and Computer Science, University of Catania, Catania, Italy
Venue:
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Year:
2005

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Abstract

In this work we investigate the applicability of a multiobjective 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, mut1 and mut2, 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.