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Algorithms
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Nature-Inspired Algorithms in Machine Learning (2nd Edition)
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Nature-Inspired Algorithms in Machine Learning (2nd Edition)

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 8585

Special Issue Editors

Dr. Szymon Łukasik

E-Mail Website
Guest Editor
Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, 30-059 Kraków, Poland
Interests: computational intelligence; data mining; metaheuristics; dimensionality reduction; unsupervised learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Piotr A. Kowalski

E-Mail Website
Guest Editor
Faculty of Physics and Applied Computer Science, AGH University of Krakow, 30-059 Krakow, Poland
Interests: data science; artificial neural networks; metaheuristics; swarm intelligence; evolutionary computation; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Rohit Salgotra

E-Mail Website
Guest Editor
Faculty of Physics & Applied Computer Science, AGH University of Science & Technology, 30-059 Kraków, Poland
Interests: nature inspired algorithms and their applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We cordially invite you to submit your papers to the Special Issue “Nature-Inspired Algorithms in Machine Learning” of Algorithms, an established MPDI journal indexed—among others—in Clarivate Web of Science and Scopus. 

Machine learning algorithms are currently omnipresent in a variety of practical solutions spanning from space engineering to e-commerce. Apart from the standard statistical approach nature-inspired algorithms are also frequently used in this area. It is due to the complexity of the data exploration tasks and the possibility of including additional factors into the scheme of nature-inspired algorithm. 

Our Special Issue will accept a broad range of new advances in the field of nature-inspired machine learning algorithms. We invite contributions describing new techniques, novel evaluation criteria, interesting case-studies, as well as papers dealing with specific variants of existing algorithms and challenges of Big Data. A limited number of state-of-art reviews will also be considered for publication. 

Please feel free to contribute as well as to contact us with any questions and concerns.

Dr. Szymon Łukasik
Dr. Piotr A. Kowalski
Dr. Rohit Salgotra
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data science/data mining
  • clustering
  • classification
  • outlier detection
  • dimensionality reduction
  • unsupervised learning
  • supervised learning
  • nature-inspired algorithms
  • metaheuristics

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Related Special Issue

Published Papers (6 papers)

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Research

19 pages, 1774 KiB  
Article
A Novel Approach to Predict the Asian Exchange Stock Market Index Using Artificial Intelligence
by Rohit Salgotra, Harmanjeet Singh, Gurpreet Kaur, Supreet Singh, Pratap Singh and Szymon Lukasik
Algorithms 2024, 17(10), 457; https://doi.org/10.3390/a17100457 - 15 Oct 2024
Viewed by 649
Abstract
This study uses real-world illustrations to explore the application of deep learning approaches to predict economic information. In this, we investigate the effect of deep learning model architecture and time-series data properties on prediction accuracy. We aim to evaluate the predictive power of [...] Read more.
This study uses real-world illustrations to explore the application of deep learning approaches to predict economic information. In this, we investigate the effect of deep learning model architecture and time-series data properties on prediction accuracy. We aim to evaluate the predictive power of several neural network models using a financial time-series dataset. These models include Convolutional RNNs, Convolutional LSTMs, Convolutional GRUs, Convolutional Bi-directional RNNs, Convolutional Bi-directional LSTMs, and Convolutional Bi-directional GRUs. Our main objective is to utilize deep learning techniques for simultaneous predictions on multivariable time-series datasets. We utilize the daily fluctuations of six Asian stock market indices from 1 April 2020 to 31 March 2024. This study’s overarching goal is to evaluate deep learning models constructed using training data gathered during the early stages of the COVID-19 pandemic when the economy was hit hard. We find that the limitations prove that no single deep learning algorithm can reliably forecast financial data for every state. In addition, predictions obtained from solitary deep learning models are more precise when dealing with consistent time-series data. Nevertheless, the hybrid model performs better when analyzing time-series data with significant chaos. Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms in Machine Learning (2nd Edition))
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26 pages, 15828 KiB  
Article
Optimization of Offshore Saline Aquifer CO2 Storage in Smeaheia Using Surrogate Reservoir Models
by Behzad Amiri, Ashkan Jahanbani Ghahfarokhi, Vera Rocca and Cuthbert Shang Wui Ng
Algorithms 2024, 17(10), 452; https://doi.org/10.3390/a17100452 - 11 Oct 2024
Cited by 1 | Viewed by 993
Abstract
Machine learning-based Surrogate Reservoir Models (SRMs) can replace/augment multi-physics numerical simulations by replicating the reservoir simulation results with reduced computational effort while maintaining accuracy compared with numerical simulations. This research will demonstrate SRMs’ potential in long-term simulations and optimization of geological carbon storage [...] Read more.
Machine learning-based Surrogate Reservoir Models (SRMs) can replace/augment multi-physics numerical simulations by replicating the reservoir simulation results with reduced computational effort while maintaining accuracy compared with numerical simulations. This research will demonstrate SRMs’ potential in long-term simulations and optimization of geological carbon storage in a real-world geological setting and address challenges in big data curation and model training. The present study focuses on CO2 storage in the Smeaheia saline aquifer. Two SRMs were created using Deep Neural Networks (DNNs) to predict CO2 saturation and pressure over all grid blocks for 50 years. 18 million samples and 31 features, including reservoir static and dynamic properties, build the input data. Models comprise 3–5 hidden layers with 128–512 units apiece. SRMs showed a runtime improvement of 300 times and an accuracy of 99% compared to the 3D numerical simulator. The genetic algorithm was then employed to determine the optimal rate and duration of CO2 injection, which maximizes the volume of injected CO2 while ensuring storage operations’ safety through constraints. The optimization continued for the reproduction of 100 generations, each containing 100 individuals, without any hyperparameter tuning. Finally, the optimization results confirm the significant potential of Smeaheia for storing 170 Mt CO2. Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms in Machine Learning (2nd Edition))
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20 pages, 368 KiB  
Article
Fitness Landscape Analysis of Product Unit Neural Networks
by Andries Engelbrecht and Robert Gouldie 
Algorithms 2024, 17(6), 241; https://doi.org/10.3390/a17060241 - 4 Jun 2024
Viewed by 618
Abstract
A fitness landscape analysis of the loss surfaces produced by product unit neural networks is performed in order to gain a better understanding of the impact of product units on the characteristics of the loss surfaces. The loss surface characteristics of product unit [...] Read more.
A fitness landscape analysis of the loss surfaces produced by product unit neural networks is performed in order to gain a better understanding of the impact of product units on the characteristics of the loss surfaces. The loss surface characteristics of product unit neural networks are then compared to the characteristics of loss surfaces produced by neural networks that make use of summation units. The failure of certain optimization algorithms in training product neural networks is explained through trends observed between loss surface characteristics and optimization algorithm performance. The paper shows that the loss surfaces of product unit neural networks have extremely large gradients with many deep ravines and valleys, which explains why gradient-based optimization algorithms fail at training these neural networks. Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms in Machine Learning (2nd Edition))
13 pages, 451 KiB  
Article
An Objective Function-Based Clustering Algorithm with a Closed-Form Solution and Application to Reference Interval Estimation in Laboratory Medicine
by Frank Klawonn and Georg Hoffmann
Algorithms 2024, 17(4), 143; https://doi.org/10.3390/a17040143 - 29 Mar 2024
Cited by 1 | Viewed by 1320
Abstract
Clustering algorithms are usually iterative procedures. In particular, when the clustering algorithm aims to optimise an objective function like in k-means clustering or Gaussian mixture models, iterative heuristics are required due to the high non-linearity of the objective function. This implies higher [...] Read more.
Clustering algorithms are usually iterative procedures. In particular, when the clustering algorithm aims to optimise an objective function like in k-means clustering or Gaussian mixture models, iterative heuristics are required due to the high non-linearity of the objective function. This implies higher computational costs and the risk of finding only a local optimum and not the global optimum of the objective function. In this paper, we demonstrate that in the case of one-dimensional clustering with one main and one noise cluster, one can formulate an objective function, which permits a closed-form solution with no need for an iteration scheme and the guarantee of finding the global optimum. We demonstrate how such an algorithm can be applied in the context of laboratory medicine as a method to estimate reference intervals that represent the range of “normal” values. Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms in Machine Learning (2nd Edition))
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12 pages, 826 KiB  
Article
A Markov Chain Genetic Algorithm Approach for Non-Parametric Posterior Distribution Sampling of Regression Parameters
by Parag C. Pendharkar
Algorithms 2024, 17(3), 111; https://doi.org/10.3390/a17030111 - 7 Mar 2024
Viewed by 1385
Abstract
This paper proposes a genetic algorithm-based Markov Chain approach that can be used for non-parametric estimation of regression coefficients and their statistical confidence bounds. The proposed approach can generate samples from an unknown probability density function if a formal functional form of its [...] Read more.
This paper proposes a genetic algorithm-based Markov Chain approach that can be used for non-parametric estimation of regression coefficients and their statistical confidence bounds. The proposed approach can generate samples from an unknown probability density function if a formal functional form of its likelihood is known. The approach is tested in the non-parametric estimation of regression coefficients, where the least-square minimizing function is considered the maximum likelihood of a multivariate distribution. This approach has an advantage over traditional Markov Chain Monte Carlo methods because it is proven to converge and generate unbiased samples computationally efficiently. Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms in Machine Learning (2nd Edition))
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46 pages, 21402 KiB  
Article
On the Development of Descriptor-Based Machine Learning Models for Thermodynamic Properties: Part 2—Applicability Domain and Outliers
by Cindy Trinh, Silvia Lasala, Olivier Herbinet and Dimitrios Meimaroglou
Algorithms 2023, 16(12), 573; https://doi.org/10.3390/a16120573 - 18 Dec 2023
Viewed by 2448
Abstract
This article investigates the applicability domain (AD) of machine learning (ML) models trained on high-dimensional data, for the prediction of the ideal gas enthalpy of formation and entropy of molecules via descriptors. The AD is crucial as it describes the space of chemical [...] Read more.
This article investigates the applicability domain (AD) of machine learning (ML) models trained on high-dimensional data, for the prediction of the ideal gas enthalpy of formation and entropy of molecules via descriptors. The AD is crucial as it describes the space of chemical characteristics in which the model can make predictions with a given reliability. This work studies the AD definition of a ML model throughout its development procedure: during data preprocessing, model construction and model deployment. Three AD definition methods, commonly used for outlier detection in high-dimensional problems, are compared: isolation forest (iForest), random forest prediction confidence (RF confidence) and k-nearest neighbors in the 2D projection of descriptor space obtained via t-distributed stochastic neighbor embedding (tSNE2D/kNN). These methods compute an anomaly score that can be used instead of the distance metrics of classical low-dimension AD definition methods, the latter being generally unsuitable for high-dimensional problems. Typically, in low- (high-) dimensional problems, a molecule is considered to lie within the AD if its distance from the training domain (anomaly score) is below a given threshold. During data preprocessing, the three AD definition methods are used to identify outlier molecules and the effect of their removal is investigated. A more significant improvement of model performance is observed when outliers identified with RF confidence are removed (e.g., for a removal of 30% of outliers, the MAE (Mean Absolute Error) of the test dataset is divided by 2.5, 1.6 and 1.1 for RF confidence, iForest and tSNE2D/kNN, respectively). While these three methods identify X-outliers, the effect of other types of outliers, namely Model-outliers and y-outliers, is also investigated. In particular, the elimination of X-outliers followed by that of Model-outliers enables us to divide MAE and RMSE (Root Mean Square Error) by 2 and 3, respectively, while reducing overfitting. The elimination of y-outliers does not display a significant effect on the model performance. During model construction and deployment, the AD serves to verify the position of the test data and of different categories of molecules with respect to the training data and associate this position with their prediction accuracy. For the data that are found to be close to the training data, according to RF confidence, and display high prediction errors, tSNE 2D representations are deployed to identify the possible sources of these errors (e.g., representation of the chemical information in the training data). Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms in Machine Learning (2nd Edition))
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