Analytically Tuned Simulated Annealing Applied to the Protein Folding Problem

Frausto–Solís, Juan; Román, Estela; Romero, David; Soberón, Xavier; Liñán-García, Ernesto

doi:10.1007/978-3-540-72586-2_53

Cited by 11 publications

(14 citation statements)

References 27 publications

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“…QSA [ 9 ] is a version of Simulated Annealing (SA), which is a general purpose randomized metaheuristic that finds good approximations to the optimal solution for large combinatorial problems. QSA was used to select different random feature subsets from the dataset.…”

Section: Methodsmentioning

confidence: 99%

A Predictive Model for Guillain-Barré Syndrome Based on Single Learning Algorithms

Canul-Reich

Frausto–Solís

Hernández-Torruco

2017

Computational and Mathematical Methods in Medicine

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Background. Guillain-Barré Syndrome (GBS) is a potentially fatal autoimmune neurological disorder. The severity varies among the four main subtypes, named as Acute Inflammatory Demyelinating Polyneuropathy (AIDP), Acute Motor Axonal Neuropathy (AMAN), Acute Motor Sensory Axonal Neuropathy (AMSAN), and Miller-Fisher Syndrome (MF). A proper subtype identification may help to promptly carry out adequate treatment in patients. Method. We perform experiments with 15 single classifiers in two scenarios: four subtypes' classification and One versus All (OvA) classification. We used a dataset with the 16 relevant features identified in a previous phase. Performance evaluation is made by 10-fold cross validation (10-FCV). Typical classification performance measures are used. A statistical test is conducted in order to identify the top five classifiers for each case. Results. In four GBS subtypes' classification, half of the classifiers investigated in this study obtained an average accuracy above 0.90. In OvA classification, the two subtypes with the largest number of instances resulted in the best classification results. Conclusions. This study represents a comprehensive effort on creating a predictive model for Guillain-Barré Syndrome subtypes. Also, the analysis performed in this work provides insight about the best single classifiers for each classification case.

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Section: Methodsmentioning

confidence: 99%

A Predictive Model for Guillain-Barré Syndrome Based on Single Learning Algorithms

Canul-Reich

Frausto–Solís

Hernández-Torruco

2017

Computational and Mathematical Methods in Medicine

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“…This method has been used in many combinatorial optimization problems [9, 10, 30–32]. However, SA has a low convergence feature and requires too much execution time.…”

Section: Methodsmentioning

confidence: 99%

“…However, in order to generate high-quality solutions for PFP, new and more efficient SA should be designed [9]; one of them is named Multiquenching Annealing algorithm (MQA) [10]. This algorithm uses two phases.…”

Section: Introductionmentioning

confidence: 99%

Chaotic Multiquenching Annealing Applied to the Protein Folding Problem

Frausto–Solís

Liñán-García

Sánchez-Pérez

et al. 2014

The Scientific World Journal

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The Chaotic Multiquenching Annealing algorithm (CMQA) is proposed. CMQA is a new algorithm, which is applied to protein folding problem (PFP). This algorithm is divided into three phases: (i) multiquenching phase (MQP), (ii) annealing phase (AP), and (iii) dynamical equilibrium phase (DEP). MQP enforces several stages of quick quenching processes that include chaotic functions. The chaotic functions can increase the exploration potential of solutions space of PFP. AP phase implements a simulated annealing algorithm (SA) with an exponential cooling function. MQP and AP are delimited by different ranges of temperatures; MQP is applied for a range of temperatures which goes from extremely high values to very high values; AP searches for solutions in a range of temperatures from high values to extremely low values. DEP phase finds the equilibrium in a dynamic way by applying least squares method. CMQA is tested with several instances of PFP.

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“…The search is performed by using a hybrid approach [19], which is composed of an initial search performed by a SA algorithm, and followed by a localized refinement of the solution with a sequential quadratic programming (SQP) algorithm. The main settings of the SA algorithm were based on existing studies [20][21][22], as follows:…”

Section: Proposed Algorithmmentioning

confidence: 99%

A Simulated Annealing Based Optimization Algorithm

Tenne¹

2017

Computational Optimization in Engineering - Paradigms and Applications

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Analytically Tuned Simulated Annealing Applied to the Protein Folding Problem

Cited by 11 publications

References 27 publications

A Predictive Model for Guillain-Barré Syndrome Based on Single Learning Algorithms

A Predictive Model for Guillain-Barré Syndrome Based on Single Learning Algorithms

Chaotic Multiquenching Annealing Applied to the Protein Folding Problem

A Simulated Annealing Based Optimization Algorithm

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