Genetic Algorithm for 3D Protein Folding Simulations
Proceedings of the 5th International Conference on Genetic Algorithms
A Standard GA Approach to Native Protein Conformation Prediction
Proceedings of the 6th International Conference on Genetic Algorithms
Application of Evolutionary Algorithms to Protein Folding Prediction
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
Protein Structure Prediction Using Evolutionary Algorithms Hybridized with Backtracking
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Discovering promising regions to help global numerical optimization algorithms
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Investigating relevant aspects of MOEAs for protein structures prediction
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Information Sciences: an International Journal
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Protein structure prediction (PSP) is a computational complex problem. To work with large proteins, simple protein models have been employed to represent the conformation, and evolutionary algorithms (EAs) are usually used to search for adequate solutions. However, the generation of unfeasible conformations may decrease the EA performance. For this reason, this paper presents two alternative representations that reduce the number of improper structures, improving the search process. Both representations have been investigated in terms of initial population in order to start the evolutionary process with promising regions. The results have shown a significant improvement in the fitness values (or, in other words, in solution quality).