A complete and effective move set for simplified protein folding
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Genetic Algorithm for 3D Protein Folding Simulations
Proceedings of the 5th International Conference on Genetic Algorithms
Protein structure prediction in lattice models with particle swarm optimization
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
An evolutionary model based on hill-climbing search operators for protein structure prediction
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Hybrid evolutionary algorithm with a composite fitness function for protein structure prediction
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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This paper presents a new Genetic Algorithm for Protein Structure Prediction problem in both 2D and 3D hydrophobic-hydrophilic lattice models, introduced in [1]. Our algorithm evolves a new local-search genetic operation (called Pull-Move and well described in [2]), into the standard GA1 ([3,4]). The experiments show that performing a set of Pull-Moves in addition to standard genetic operations in GA (such as crossover and mutation) leads to significant energy improvements. The paper also introduces the Global Energy as fitness function and explains the advantages of utilizing it rather than the standard Free Energy. The experimental results are even more impressive when using the Global Energy as fitness function in GA.