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Applied Sciences
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Bio-Inspired Computational Techniques: Theory, Methods and Applications
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Bio-Inspired Computational Techniques: Theory, Methods and Applications

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 (10 November 2021) | Viewed by 8466

Special Issue Editors

Prof. Dr. Mario Dante Lucio Giacobini

E-Mail Website
Guest Editor
Department of Veterinary Sciences, University of Torino, largo Paolo Braccini, 2, 10095 Grugliasco TO, Italy
Interests: computational epidemiology; evolutionary computation; computational biology; artificial life; complex systems
Prof. Dr. Leonardo Vanneschi

E-Mail Website
Guest Editor
NOVA Information Management School (NOVA IMS), Universidade Nova of Lisbon, Campus de Campolide, 1070-312 Lisboa, Portugal
Interests: machine learning; genetic programming; particle swarm optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since the early days of Artificial Intelligence, nature has been seen as a great source of inspiration to provide solutions for complex problems. Under evolutionary pressure, aggregations of cells, tissues, organisms, and species are pushed to develop optimized skills for their survival, finding solutions that are often creative and unexpected. These emergent behaviors can be emulated to design computational techniques for tackling real-world problems. For over 70 years, bio-inspired algorithms represent a research area that takes inspiration from natural phenomena, in order to implement high-performance computing approaches and intelligent paradigms, with the aim of solving complex problems. Examples of bio-inspired computational techniques include, but are not limited to, artificial neural networks, evolutionary computation, swarm intelligence and ant colonies, just to mention a few. Bio-inspired computation not only results in a fruitful approach for solving real-world problems, but it also plays a fundamental role in artificial life, at the intersection between biology, mathematics, and computer science. Thanks to these approaches, models of natural phenomena can be built to interfere with their behavior.

The objective of this Special Issue is to collect original research articles as well as review articles that will draw the current framework of research in the area of bio-inspired algorithms and approaches to solve problems or to model phenomena in different domains.

Prof. Dr. Mario Dante Lucio Giacobini
Prof. Dr. Leonardo Vanneschi
Guest Editors

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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.

Keywords

  • bio-inspired computing
  • artificial neural networks
  • evolutionary computation
  • swarm intelligence
  • algorithms
  • theoretical foundations
  • real-life applications

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Published Papers (3 papers)

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Research

16 pages, 334 KiB  
Article
From Requirements to Source Code: Evolution of Behavioral Programs
by Roy Poliansky, Moshe Sipper and Achiya Elyasaf
Appl. Sci. 2022, 12(3), 1587; https://doi.org/10.3390/app12031587 - 2 Feb 2022
Cited by 3 | Viewed by 2918
Abstract
Automatically generating executable code has a long history of arguably modest success, mostly limited to the generation of small programs of up to 200 lines of code, and genetic improvement of existing code. We present the use of genetic programming (GP) in conjunction [...] Read more.
Automatically generating executable code has a long history of arguably modest success, mostly limited to the generation of small programs of up to 200 lines of code, and genetic improvement of existing code. We present the use of genetic programming (GP) in conjunction with context-oriented behavioral programming (COBP), the latter being a programming paradigm with unique characteristics that facilitate automatic coding. COBP models a program as a set of behavioral threads (b-threads), each aligned to a single behavior or requirement of the system. To evolve behavioral programs we design viable and effective genetic operators, a genetic representation, and evaluation methods. The simplicity of the COBP paradigm, its straightforward syntax, the ability to use verification and formal-method techniques to validate program correctness, and a program comprising small independent chunks all allow us to effectively generate behavioral programs using GP. To demonstrate our approach we evolve complete programs from scratch of a highly competent O player for the game of tic-tac-toe. The evolved programs are well structured, consisting of multiple, explainable modules that specify the different behavioral aspects of the program and are similar to our handcrafted program. To validate the correctness of our individuals, we utilize the mathematical characteristics of COBP to analyze program behavior under all possible execution paths. Our analysis of an evolved program proved that it plays as expected more than 99% of the times. Full article
(This article belongs to the Special Issue Bio-Inspired Computational Techniques: Theory, Methods and Applications)
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16 pages, 411 KiB  
Article
Towards a Vectorial Approach to Predict Beef Farm Performance
by Francesca Abbona, Leonardo Vanneschi and Mario Giacobini
Appl. Sci. 2022, 12(3), 1137; https://doi.org/10.3390/app12031137 - 21 Jan 2022
Viewed by 1846
Abstract
Accurate livestock management can be achieved by means of predictive models. Critical factors affecting the welfare of intensive beef cattle husbandry systems can be difficult to be detected, and Machine Learning appears as a promising approach to investigate the hundreds of variables and [...] Read more.
Accurate livestock management can be achieved by means of predictive models. Critical factors affecting the welfare of intensive beef cattle husbandry systems can be difficult to be detected, and Machine Learning appears as a promising approach to investigate the hundreds of variables and temporal patterns lying in the data. In this article, we explore the use of Genetic Programming (GP) to build a predictive model for the performance of Piemontese beef cattle farms. In particular, we investigate the use of vectorial GP, a recently developed variant of GP, that is particularly suitable to manage data in a vectorial form. The experiments conducted on the data from 2014 to 2018 confirm that vectorial GP can outperform not only the standard version of GP but also a number of state-of-the-art Machine Learning methods, such as k-Nearest Neighbors, Generalized Linear Models, feed-forward Neural Networks, and long- and short-term memory Recurrent Neural Networks, both in terms of accuracy and generalizability. Moreover, the intrinsic ability of GP in performing an automatic feature selection, while generating interpretable predictive models, allows highlighting the main elements influencing the breeding performance. Full article
(This article belongs to the Special Issue Bio-Inspired Computational Techniques: Theory, Methods and Applications)
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13 pages, 2819 KiB  
Article
An Improved Dingo Optimization Algorithm Applied to SHE-PWM Modulation Strategy
by Juan H. Almazán-Covarrubias, Hernán Peraza-Vázquez, Adrián F. Peña-Delgado and Pedro Martín García-Vite
Appl. Sci. 2022, 12(3), 992; https://doi.org/10.3390/app12030992 - 19 Jan 2022
Cited by 32 | Viewed by 2841
Abstract
This paper presents a modification to the dingo optimization algorithm (mDOA) to solve the non-linear set of equations of the selective harmonic elimination (SHE) control technique widely applied in multilevel inverters. In addition, said modification is conducted to the survival criteria by including [...] Read more.
This paper presents a modification to the dingo optimization algorithm (mDOA) to solve the non-linear set of equations of the selective harmonic elimination (SHE) control technique widely applied in multilevel inverters. In addition, said modification is conducted to the survival criteria by including a local search to provide a better balance when replacing vectors (dingoes) with a low survival rate. The proposed method is also benchmarked with some unimodal functions to illustrate its better exploitation capabilities. Finally, the SHE optimization results were calculated and compared with three well-known state-of-the-art metaheuristics, where the modified version of the dingo optimization algorithm showed very competitive results. The significant difference between the mDOA results and the rest of the algorithms is determined by the Wilcoxon rank-sum non-parametric statistical test with a 5% degree of significance. The p-values confirm the meaningful advantage of the mDOA compared to other bio-inspired algorithms for many modulation indexes. Experimentally, the proposed algorithm is validated through the implementation of a three-phase 11-level inverter. Full article
(This article belongs to the Special Issue Bio-Inspired Computational Techniques: Theory, Methods and Applications)
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