Algorithm Selection for the Team Orienteering Problem

Mısır, Mustafa; Gunawan, Aldy; Vansteenwegen, Pieter

doi:10.1007/978-3-031-04148-8_3

Cited by 8 publications

(2 citation statements)

References 45 publications

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“…ALORS is an algorithm recommendation system, based on collaborative filtering (CF) [39]. It has been successfully applied for different selection decisions on varying problem domains [40,41,42,43], including those on a relevant protein-structure prediction problem [44,45]. CF is a type of recommendation approach, that predicts how much users like certain items such as movies and products.…”

Section: Methodsmentioning

confidence: 99%

Algorithm Selection for Protein-Ligand Docking: A Case Study on ACE with AutoDock

Chen

Zhou

Shu

et al. 2022

Preprint

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The present study investigates the use of algorithm selection for automatically choosing an algorithm for any given protein-ligand docking task. In drug discovery and design process, conceptualizing protein-ligand binding is a major problem. Targeting this problem through computational methods is beneficial in order to substantially reduce the resource and time requirements for the overall drug development process. One way of addressing protein-ligand docking is to model it as a search and optimization problem. There have been a variety of algorithmic solutions in this respect. However, there is no ultimate algorithm that can efficiently tackle this problem, both in terms of protein-ligand docking quality and speed. This argument motivates devising new algorithms, tailored to the particular protein-ligand docking scenarios. To this end, this paper reports a machine learning-based approach for improved and robust docking performance. The proposed set-up is fully automated, operating without any expert opinion or involvement both on the problem and algorithm aspects. As a case study, an empirical analysis was performed on a well-known protein, Human Angiotensin-Converting Enzyme (ACE), with 1428 ligands. For general applicability, AutoDock 4.2 was used as the docking platform. The candidate algorithms are also taken from AutoDock 4.2. Twenty-eight distinctly configured Lamarckian-Genetic Algorithm (LGA) are chosen to build an algorithm set. ALORS which is a recommender system-based algorithm selection system was preferred for automating the selection from those LGA variants on a per-instance basis. For realizing this selection automation, molecular descriptors and substructure fingerprints were employed as the features characterizing each target protein-ligand docking instance. The computational results revealed that algorithm selection outperforms all those candidate algorithms. Further assessment is reported on the algorithms space, discussing the contributions of LGA’s parameters. As it pertains to protein-ligand docking, the contributions of the aforementioned features are examined, which shed light on the critical features affecting the docking performance.

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Section: Methodsmentioning

confidence: 99%

Algorithm Selection for Protein-Ligand Docking: A Case Study on ACE with AutoDock

Chen

Zhou

Shu

et al. 2022

Preprint

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“…Leyton-Brown et al also proposed an algorithm portfolio approach, reporting that a set of algorithms with a selection heuristic collectively outperforms its constituent algorithms on the combinatorial auction Winner Determination Problem (WDP) [24]. The algorithm portfolio approach has been successfully applied to solving various problems, including propositional satisfiability [25,26], automated mission planning [27], university timetabling [28], traveling salesman [29], subgraph isomorphisms [30], collaborative filtering [31], dynamic maximal covering location [32], and human behavior and syllogistic reasoning [33]. The main advantage of combining algorithms in a portfolio is the reduction of computational cost while boosting classification accuracy [23].…”

Section: Related Work 21 Algorithm Portfoliosmentioning

confidence: 99%

Personalized Classifier Selection for EEG-Based BCIs

Rahimipour Anaraki,

Kolokolova,

Chau

2024

Computers

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The most important component of an Electroencephalogram (EEG) Brain–Computer Interface (BCI) is its classifier, which translates EEG signals in real time into meaningful commands. The accuracy and speed of the classifier determine the utility of the BCI. However, there is significant intra- and inter-subject variability in EEG data, complicating the choice of the best classifier for different individuals over time. There is a keen need for an automatic approach to selecting a personalized classifier suited to an individual’s current needs. To this end, we have developed a systematic methodology for individual classifier selection, wherein the structural characteristics of an EEG dataset are used to predict a classifier that will perform with high accuracy. The method was evaluated using motor imagery EEG data from Physionet. We confirmed that our approach could consistently predict a classifier whose performance was no worse than the single-best-performing classifier across the participants. Furthermore, Kullback–Leibler divergences between reference distributions and signal amplitude and class label distributions emerged as the most important characteristics for classifier prediction, suggesting that classifier choice depends heavily on the morphology of signal amplitude densities and the degree of class imbalance in an EEG dataset.

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A Stacked Autoencoder Based Meta-Learning Model for Global Optimization

Ma,

Pang,

et al. 2023

Communications in Computer and Information Science

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Algorithm Selection for the Team Orienteering Problem

Cited by 8 publications

References 45 publications

Algorithm Selection for Protein-Ligand Docking: A Case Study on ACE with AutoDock

Algorithm Selection for Protein-Ligand Docking: A Case Study on ACE with AutoDock

Personalized Classifier Selection for EEG-Based BCIs

A Stacked Autoencoder Based Meta-Learning Model for Global Optimization

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