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Applied Sciences
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Applications of Evolutionary Computation to Machine Learning and Data Mining
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Applications of Evolutionary Computation to Machine Learning and Data Mining

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 11594

Special Issue Editors

Dr. Michael Affenzeller

E-Mail Website
Guest Editor
Heuristic and Evolutionary Algorithms Laboratory (HEAL), University of Applied Sciences Upper Austria—Campus Hagenberg, Softwarepark 11, 4232 Hagenberg, Austria
Interests: evolutionary computation; genetic algorithms; genetic programming; machine learning; data-based modeling; explainable ai; prescriptive analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Kaifeng Yang

E-Mail Website
Guest Editor
School of Informatics, Communications and Media University of Applied Sciences Upper Austria Softwarepark 13, A-4232 Hagenberg, Austria
Interests: evolutionary algorithms; machine learning; data mining

Special Issue Information

Dear Colleagues,

The potential interactions between evolutionary computation and machine learning are manifold and are further advanced by recent developments in the areas of human-centered AI as well as interpretable and explainable AI. Application perspectives often arise in but are not limited to manufacturing, logistics and product design. In terms of applications, there are strong connections to industrial AI, and in terms of methods and algorithms, there is a close relationship with predictive and prescriptive analytics.

A particularly prominent example of this field is symbolic regression, in which complex nonlinear relationships can be identified from data using genetic programming, whereby the models are mathematical formulas that can be interpreted. More and more example applications show that approaches such as these are often superior exactly when the system to be described by data has a natural scientific background, where mathematics can particularly bring out its special strengths as a universal descriptive language.

In the data-based generation of surrogate models, as often practiced in simulation-based optimization, evolutionary algorithms can be used both on the optimization side and on the machine-learning side. Further examples in this environment are the topology optimization of neural networks with genetic algorithms or the anticipation of dynamic systems’ behavior in optimization by means of machine learning.

Dr. Michael Affenzelle
Dr. Kaifeng Yang
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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.

Published Papers (4 papers)

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Research

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35 pages, 3130 KiB  
Article
Co-Operative Binary Bat Optimizer with Rough Set Reducts for Text Feature Selection
by Aisha Adel, Nazlia Omar, Salwani Abdullah and Adel Al-Shabi
Appl. Sci. 2022, 12(21), 11296; https://doi.org/10.3390/app122111296 - 07 Nov 2022
Cited by 3 | Viewed by 1341
Abstract
The process of eliminating irrelevant, redundant and noisy features while trying to maintain less information loss is known as a feature selection problem. Given the vast amount of the textual data generated and shared on the internet such as news reports, articles, tweets [...] Read more.
The process of eliminating irrelevant, redundant and noisy features while trying to maintain less information loss is known as a feature selection problem. Given the vast amount of the textual data generated and shared on the internet such as news reports, articles, tweets and product reviews, the need for an effective text-feature selection method becomes increasingly important. Recently, stochastic optimization algorithms have been adopted to tackle this problem. However, the efficiency of these methods is decreased when tackling high-dimensional problems. This decrease could be attributed to premature convergence where the population diversity is not well maintained. As an innovative attempt, a cooperative Binary Bat Algorithm (BBACO) is proposed in this work to select the optimal text feature subset for classification purposes. The proposed BBACO uses a new mechanism to control the population’s diversity during the optimization process and to improve the performance of BBA-based text-feature selection method. This is achieved by dividing the dimension of the problem into several parts and optimizing each of them in a separate sub-population. To evaluate the generality and capability of the proposed method, three classifiers and two standard benchmark datasets in English, two in Malay and one in Arabic were used. The results show that the proposed method steadily improves the classification performance in comparison with other well-known feature selection methods. The improvement is obtained for all of the English, Malay and Arabic datasets which indicates the generality of the proposed method in terms of the dataset language. Full article
(This article belongs to the Special Issue Applications of Evolutionary Computation to Machine Learning and Data Mining)
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10 pages, 1344 KiB  
Article
Hybrid GA-SVM Approach for Postoperative Life Expectancy Prediction in Lung Cancer Patients
by Arfan Ali Nagra, Iqra Mubarik, Muhammad Mugees Asif, Khalid Masood, Mohammed A. Al Ghamdi and Sultan H. Almotiri
Appl. Sci. 2022, 12(21), 10927; https://doi.org/10.3390/app122110927 - 28 Oct 2022
Cited by 7 | Viewed by 1345
Abstract
Medical outcomes must be tracked in order to enhance quality initiatives, healthcare management, and mass education. Thoracic surgery data have been acquired for those who underwent major lung surgery for primary lung cancer, a field in which there has been little research and [...] Read more.
Medical outcomes must be tracked in order to enhance quality initiatives, healthcare management, and mass education. Thoracic surgery data have been acquired for those who underwent major lung surgery for primary lung cancer, a field in which there has been little research and few reliable recommendations have been made for lung cancer patients. Early detection of lung cancer benefits therapy choices and increases the odds of a patient surviving a lung cancer infection. Using a Hybrid Genetic and Support Vector Machine (GA-SVM) methodology, this study proposes a method for identifying lung cancer patients. To estimate postoperative life expectancy, ensemble machine-learning techniques were applied. The article also presents a strategy for estimating a patient’s life expectancy following thoracic surgery after the detection of cancer. To perform the prediction, hybrid machine-learning methods were applied. In ensemble machine-learning algorithms, attribute ranking and selection are critical components of robust health outcome prediction. To enhance the efficacy of algorithms in health data analysis, we propose three attribute ranking and selection procedures. Compared to other machine-learning techniques, GA-SVM achieves an accuracy of 85% and a higher F1 score of 0.92. The proposed algorithm was compared with two recent state-of-the-art techniques and its performance level was ranked superior to those of its counterparts. Full article
(This article belongs to the Special Issue Applications of Evolutionary Computation to Machine Learning and Data Mining)
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18 pages, 1442 KiB  
Article
Optimization and Diversification of Cryptocurrency Portfolios: A Composite Copula-Based Approach
by Herve M. Tenkam, Jules C. Mba and Sutene M. Mwambi
Appl. Sci. 2022, 12(13), 6408; https://doi.org/10.3390/app12136408 - 23 Jun 2022
Cited by 7 | Viewed by 1753
Abstract
This paper focuses on the selection and optimisation of a cryptoasset portfolio, using the K-means clustering algorithm and GARCH C-Vine copula model combined with the differential evolution algorithm. This integrated approach allows the construction of a diversified portfolio of eight cryptocurrencies and determines [...] Read more.
This paper focuses on the selection and optimisation of a cryptoasset portfolio, using the K-means clustering algorithm and GARCH C-Vine copula model combined with the differential evolution algorithm. This integrated approach allows the construction of a diversified portfolio of eight cryptocurrencies and determines an optimal allocation strategy making it possible to minimize the conditional value-at-risk of the portfolio and maximise the return. Our results show that stablecoins such as True-USD are negatively correlated to the other cryptoassets in the portfolio and could therefore be a safe haven for crypto-investors during market turmoil. Our findings are in line with previous studies exhibiting stablecoins as potential diversifiers. Full article
(This article belongs to the Special Issue Applications of Evolutionary Computation to Machine Learning and Data Mining)
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Other

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30 pages, 24538 KiB  
Systematic Review
A Systematic Review and Analysis of Intelligence-Based Pathfinding Algorithms in the Field of Video Games
by Sharmad Rajnish Lawande, Graceline Jasmine, Jani Anbarasi and Lila Iznita Izhar
Appl. Sci. 2022, 12(11), 5499; https://doi.org/10.3390/app12115499 - 28 May 2022
Cited by 7 | Viewed by 6410
Abstract
This paper provides a performance comparison of different pathfinding Algorithms used in video games. The Algorithms have been classified into three categories: informed, uninformed, and metaheuristic. Both a practical and a theoretical approach have been adopted in this paper. The practical approach involved [...] Read more.
This paper provides a performance comparison of different pathfinding Algorithms used in video games. The Algorithms have been classified into three categories: informed, uninformed, and metaheuristic. Both a practical and a theoretical approach have been adopted in this paper. The practical approach involved the implementation of specific Algorithms such as Dijkstra’s, A-star, Breadth First Search, and Greedy Best First. The comparison of these Algorithms is based on different criteria including execution time, total number of iterations, shortest path length, and grid size. For the theoretical approach, information was collected from various papers to compare other Algorithms with the implemented ones. The Unity game engine was used in implementing the Algorithms. The environment used was a two-dimensional grid system. Full article
(This article belongs to the Special Issue Applications of Evolutionary Computation to Machine Learning and Data Mining)
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