Proceedings of the Third European Conference on Advances in Artificial Life
A Standard GA Approach to Native Protein Conformation Prediction
Proceedings of the 6th International Conference on Genetic Algorithms
Cellular Automata: A Discrete Universe
Cellular Automata: A Discrete Universe
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
A genetic algorithm with backtracking for protein structure prediction
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Protein 3D HP model folding simulation based on ACO
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
A novel ab-initio genetic-based approach for protein folding prediction
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Differential evolution for protein structure prediction using the HP model
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
A critical view of the evolutionary design of self-assembling systems
EA'05 Proceedings of the 7th international conference on Artificial Evolution
An Immune Algorithm for Protein Structure Prediction on Lattice Models
IEEE Transactions on Evolutionary Computation
Multiobjectivizing the HP model for protein structure prediction
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
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In the difficult ab initio prediction in protein folding only the information of the primary structure of amino acids is used to determine the final folded conformation. The complexity of the interactions and the nature of the amino acid elements are reduced with the use of lattice models like HP, which categorizes the amino acids regarding their hydrophobicity. On the contrary to the intense research performed on the direct prediction of the final folded conformation, our aim here is to model the dynamic and emergent folding process through time, using the scheme of cellular automata but implemented with artificial neural networks optimized with Differential Evolution. Moreover, as the iterative folding also provides the final folded conformation, we can compare the results with those from direct prediction methods of the final protein conformation.