Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Investigations into the Effect of Multiobjectivization in Protein Structure Prediction
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
A Multiobjective Evolutionary Algorithm with Node-Depth Encoding for Energy Restoration
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 06
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
BSB '09 Proceedings of the 4th Brazilian Symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology
Multiobjectivizing the HP model for protein structure prediction
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
Locality-based multiobjectivization for the HP model of protein structure prediction
Proceedings of the 14th annual conference on Genetic and evolutionary computation
An improved multiobjectivization strategy for HP model-based protein structure prediction
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
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Several computational models have been developed in the context of the Protein Structure Prediction (PSP) problem. These methods involve a combinatorial problem and can be solved using optimizing algorithms in order to search for a global minimum energy. Genetic Algorithms (GAs) have produced relevant results in this area. Several energies in the protein are known to be directly responsible for the stabilization of their structures. These energies can represent each objective of multiobjective evolutionary algorithms. Many techniques, as the NSGA-II, are used to deal with the multi-objective approach for proteins, however they are not adequate for the PSP problem. New strategies have been sought with multiple criteria. In this context, this paper introduces the application of multiobjective evolutionary algorithm on tables algorithm to the PSP problem. In order to evaluate this approach, we compare it with the well-known NSGA-II algorithm. The new approach investigated for PSP can generate protein structures with energies significantly smaller than those generated by the NSGA-II.