Genetic algorithms with local search optimization for protein structure prediction problem

Berenboym, Igor; Avigal, Mireille

doi:10.1145/1389095.1389296

Cited by 10 publications

(15 citation statements)

References 3 publications

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“…A direct comparison between energy values obtained by the different evolutionary models emphasizes a good and competitive performance of the proposed ELF method. The results are clearly better than those reported by GAs [16] and PMGAs [1].…”

Section: Comparisons With Other Evolutionary Algorithmscontrasting

confidence: 73%

“…The resulting GA is applied for HP sequences having a length up to 50 residues. In [1], the results of standard GA for protein structure prediction are improved by a GA using pull moves [12] as a local search genetic operation in addition to standard crossover and mutation.…”

Section: Related Population-based Search Models For Pspmentioning

confidence: 99%

“…The results of the proposed ELF algorithm for the HP instances are directly compared to the best results obtained by the following related evolutionary models: the results of standard GA [16], Pull-Move GA (PMGA) [1], multimeme algorithms (MMA) [11], estimation of distribution algorithms (EDAs) [13] and immune algorithms (IA) [5]. The results of GA [16] are based on a population of 200 structures evolved over 300 generations.…”

Section: Comparisons With Other Evolutionary Algorithmsmentioning

confidence: 99%

“…PMGA [1] is included in the comparative analysis as it is an evolutionary algorithm using pull move transformations in addition to the standard genetic operators (pull moves are not applied in a hill-climbing mode like in the ELF and a specialized fitness function is used). PMGA improves the results of standard GAs for PSP but reports worse results for sequences S5, S7 and S8 compared to ELF.…”

Section: Comparisons With Other Evolutionary Algorithmsmentioning

confidence: 99%

“…PSP has been shown to be a NP-hard problem [4], [7] and many approximation methods and heuristics have been investigated to address it [17]. Existing computing approaches to PSP include evolutionary search [1], [3], [15], [16], ant colony optimization [14], memetic algorithms [11], tabu search [12] and Monte Carlo algorithms [9].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A hybrid evolutionary approach to protein structure prediction with lattice models

Chira

2011

2011 IEEE Congress of Evolutionary Computation (CEC)

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The prediction of minimum-energy protein structures starting from a sequence of amino acids is a computationally challenging problem even in simplified lattice protein models. A hybrid evolutionary model is designed and tested in the current paper to address this well-known NP-hard problem. Hill-climbing strategies are integrated in the search operators and a meaningful diversification of genetic material occurs during the population evolution. The main features of the proposed algorithm refer to a weak hill-climbing application of uniform crossover and pullmove transformations and the randomization of genetic material based on the fingerprint of the protein conformations. Numerical experiments are performed for several difficult bidimensional instances from lattice models (the hydrophobic-polar model and functional model proteins). The results are competitive with those obtained by related population-based optimization algorithms.

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Section: Comparisons With Other Evolutionary Algorithmscontrasting

confidence: 73%

Section: Related Population-based Search Models For Pspmentioning

confidence: 99%