Adding Probabilistic Dependencies to the Search of Protein Side Chain Configurations Using EDAs
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Iterative Lattice Protein Design Using Template Matching
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
A novel EDAs based method for HP model protein folding
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Analyzing the probability of the optimum in EDAs based on Bayesian networks
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
GP challenge: evolving energy function for protein structure prediction
Genetic Programming and Evolvable Machines
Guest editorial: special issue on evolutionary algorithms based on probabilistic models
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
Analyzing probabilistic models in hierarchical BOA
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
Protein structure prediction on a lattice model via multimodal optimization techniques
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A robust estimation of distribution algorithm for power electronic circuits design
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Protein structure prediction in lattice models with particle swarm optimization
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Twin Removal in Genetic Algorithms for Protein Structure Prediction Using Low-Resolution Model
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Learning factorizations in estimation of distribution algorithms using affinity propagation
Evolutionary Computation
Quantum-inspired evolutionary algorithms: a survey and empirical study
Journal of Heuristics
Affinity propagation enhanced by estimation of distribution algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Regularized k-order markov models in EDAs
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A preliminary study on EDAs for permutation problems based on marginal-based models
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Conflict resolution based global search operators for long protein structures prediction
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Evolutionary multimodal optimization using the principle of locality
Information Sciences: an International Journal
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
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
Regularized continuous estimation of distribution algorithms
Applied Soft Computing
On the taxonomy of optimization problems under estimation of distribution algorithms
Evolutionary Computation
Hi-index | 0.00 |
Simplified lattice models have played an important role in protein structure prediction and protein folding problems. These models can be useful for an initial approximation of the protein structure, and for the investigation of the dynamics that govern the protein folding process. Estimation of distribution algorithms (EDAs) are efficient evolutionary algorithms that can learn and exploit the search space regularities in the form of probabilistic dependencies. This paper introduces the application of different variants of EDAs to the solution of the protein structure prediction problem in simplified models, and proposes their use as a simulation tool for the analysis of the protein folding process. We develop new ideas for the application of EDAs to the bidimensional and tridimensional (2-d and 3-d) simplified protein folding problems. This paper analyzes the rationale behind the application of EDAs to these problems, and elucidates the relationship between our proposal and other population-based approaches proposed for the protein folding problem. We argue that EDAs are an efficient alternative for many instances of the protein structure prediction problem and are indeed appropriate for a theoretical analysis of search procedures in lattice models. All the algorithms introduced are tested on a set of difficult 2-d and 3-d instances from lattice models. Some of the results obtained with EDAs are superior to the ones obtained with other well-known population-based optimization algorithms.