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Application of Evolutionary Computation
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Application of Evolutionary Computation

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 June 2021) | Viewed by 10458

Special Issue Editors

Dr. Federico Divina

E-Mail Website
Guest Editor
Computer Science Division, Pablo de Olavide University, ES-41013 Seville, Spain
Interests: data mining; big data; artificial intelligence; soft computing; bioinformatics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Francisco A. Gómez Vela

E-Mail Website
Guest Editor
School of Engineerings, Pablo de Olavide University, ES-41013 Seville, Spain
Interests: computer vision; machine learning; image and signal processing; 5G communications; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Evolutionary Computation has been applied to a wide range of real-life problems, ranging from telecommunication networks to complex systems, finance and economics, games, image analysis, evolutionary music and art, parameter optimization, bioinformatics, scheduling, and logistics.

The aim of this Special Issue is to present a collection of studies describing the latest advances in techniques and applications of Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming, Simulated Annealing, Ant Colony Optimization, and related techniques. Of particular interest is the application of these techniques to computationally difficult combinatorial problems.

We also encourage researchers to share their original work in the field of computational analysis of gene expression data. Topics of primary interest include, among others, the applications of:

  • genetic algorithms;
  • evolution strategies;
  • evolutionary programming;
  • memetic algorithms;
  • genetic programming;
  • ant colony optimization;
  • co-evolutionary algorithms;
  • artificial immune systems;
  • particle swarm optimization; and
  • classifier systems.
  • evolutionary computation;
  • soft computing;
  • real-life applications.

Prof. Dr. Federico Divina
Prof. Dr. Francisco A. Gómez Vela
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.

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Published Papers (3 papers)

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Research

16 pages, 2369 KiB  
Article
Soft Computing Paradigms to Find the Numerical Solutions of a Nonlinear Influenza Disease Model
by Zulqurnain Sabir, Ag Asri Ag Ibrahim, Muhammad Asif Zahoor Raja, Kashif Nisar, Muhammad Umar, Joel J. P. C. Rodrigues and Samy R. Mahmoud
Appl. Sci. 2021, 11(18), 8549; https://doi.org/10.3390/app11188549 - 14 Sep 2021
Cited by 7 | Viewed by 2197
Abstract
The aim of this work is to present the numerical results of the influenza disease nonlinear system using the feed forward artificial neural networks (ANNs) along with the optimization of the combination of global and local search schemes. The genetic algorithm (GA) and [...] Read more.
The aim of this work is to present the numerical results of the influenza disease nonlinear system using the feed forward artificial neural networks (ANNs) along with the optimization of the combination of global and local search schemes. The genetic algorithm (GA) and active-set method (ASM), i.e., GA-ASM, are implemented as global and local search schemes. The mathematical nonlinear influenza disease system is dependent of four classes, susceptible S(u), infected I(u), recovered R(u) and cross-immune individuals C(u). For the solutions of these classes based on influenza disease system, the design of an objective function is presented using these differential system equations and its corresponding initial conditions. The optimization of this objective function is using the hybrid computing combination of GA-ASM for solving all classes of the influenza disease nonlinear system. The obtained numerical results will be compared by the Adams numerical results to check the authenticity of the designed ANN-GA-ASM. In addition, the designed approach through statistical based operators shows the consistency and stability for solving the influenza disease nonlinear system. Full article
(This article belongs to the Special Issue Application of Evolutionary Computation)
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19 pages, 1850 KiB  
Article
Evolutionary 3D Image Segmentation of Curve Epithelial Tissues of Drosophila melanogaster
by Carlos Capitán-Agudo, Beatriz Pontes, Pedro Gómez-Gálvez and Pablo Vicente-Munuera
Appl. Sci. 2021, 11(14), 6410; https://doi.org/10.3390/app11146410 - 12 Jul 2021
Viewed by 2477
Abstract
Analysing biological images coming from the microscope is challenging; not only is it complex to acquire the images, but also the three-dimensional shapes found on them. Thus, using automatic approaches that could learn and embrace that variance would be highly interesting for the [...] Read more.
Analysing biological images coming from the microscope is challenging; not only is it complex to acquire the images, but also the three-dimensional shapes found on them. Thus, using automatic approaches that could learn and embrace that variance would be highly interesting for the field. Here, we use an evolutionary algorithm to obtain the 3D cell shape of curve epithelial tissues. Our approach is based on the application of a 3D segmentation algorithm called LimeSeg, which is a segmentation software that uses a particle-based active contour method. This program needs the fine-tuning of some hyperparameters that could present a long number of combinations, with the selection of the best parametrisation being highly time-consuming. Our evolutionary algorithm automatically selects the best possible parametrisation with which it can perform an accurate and non-supervised segmentation of 3D curved epithelial tissues. This way, we combine the segmentation potential of LimeSeg and optimise the parameters selection by adding automatisation. This methodology has been applied to three datasets of confocal images from Drosophila melanogaster, where a good convergence has been observed in the evaluation of the solutions. Our experimental results confirm the proper performing of the algorithm, whose segmented images have been compared to those manually obtained for the same tissues. Full article
(This article belongs to the Special Issue Application of Evolutionary Computation)
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27 pages, 2013 KiB  
Article
Evolving a Multi-Classifier System for Multi-Pitch Estimation of Piano Music and Beyond: An Application of Cartesian Genetic Programming
by Rolando Miragaia, Francisco Fernández, Gustavo Reis and Tiago Inácio
Appl. Sci. 2021, 11(7), 2902; https://doi.org/10.3390/app11072902 - 24 Mar 2021
Cited by 5 | Viewed by 2706
Abstract
This paper presents a new method with a set of desirable properties for multi-pitch estimation of piano recordings. We propose a framework based on a set of classifiers to analyze audio input and to identify piano notes present in a given audio signal. [...] Read more.
This paper presents a new method with a set of desirable properties for multi-pitch estimation of piano recordings. We propose a framework based on a set of classifiers to analyze audio input and to identify piano notes present in a given audio signal. Our system’s classifiers are evolved using Cartesian genetic programming: we take advantage of Cartesian genetic programming to evolve a set of mathematical functions that act as independent classifiers for piano notes. Two significant improvements are described: the use of a harmonic mask for better fitness values and a data augmentation process for improving the training stage. The proposed approach achieves competitive results using F-measure metrics when compared to state-of-the-art algorithms. Then, we go beyond piano and show how it can be directly applied to other musical instruments, achieving even better results. Our system’s architecture is also described to show the feasibility of its parallelization and its implementation as a real-time system. Our methodology is also a white-box optimization approach that allows for clear analysis of the solutions found and for researchers to learn and test improvements based on the new findings. Full article
(This article belongs to the Special Issue Application of Evolutionary Computation)
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