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Signal Processing, Grammatical Evolution and Artificial Intelligence of Signals
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Signal Processing, Grammatical Evolution and Artificial Intelligence of Signals

A special issue of Signals (ISSN 2624-6120).

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 9845

Special Issue Editor

Dr. Ioannis G. Tsoulos

E-Mail Website
Guest Editor
Department of Informatics and Telecommunications, University of Ioannina, 45110 Ioannina, Greece
Interests: genetic algorithms; genetic programming; optimization; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

It is my pleasure to invite you to submit your valuable work to this Special Issue on “Signal Processing, Grammatical Evolution and Artificial Intelligence of Signals” of the reputable MDPI journal Signals.

The aim of this Special Issue is to present recent advances and extensions in grammatical evolution, application of the method in scientific and practical areas, and software specialized to grammatical evolution, as well as artificial intelligence methods applied to signal processing.

This Special Issue aims to highlight advances in signal processing, grammatical evolution and artificial intelligence in relation to signals. Topics include but are not limited to:

  • Application of grammatical evolution in signal processing;
  • Evolution of neural networks using grammatical evolution;
  • Feature construction using grammatical evolution;
  • Parallel techniques for grammatical evolution;
  • Robotics and grammatical evolution;
  • Self-organizing maps and grammatical evolution;
  • Software specialized to grammatical evolution and its applications;
  • Language inference with grammatical evolution;
  • Usage of artificial intelligence techniques on signal processing.

Dr. Ioannis Tsoulos
Guest Editor

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. Signals is an international peer-reviewed open access quarterly 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 1000 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

  • artificial intelligence
  • signal processing
  • grammatical evolution
  • high performance computing
  • artificial neural networks
  • robotics
  • language inference

Published Papers (4 papers)

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Research

13 pages, 398 KiB  
Article
Building Greibach Normal Form Grammars Using Genetic Algorithms
by Nikolaos Anastasopoulos and Evangelos Dermatas
Signals 2022, 3(4), 708-720; https://doi.org/10.3390/signals3040042 - 12 Oct 2022
Viewed by 1598
Abstract
Grammatical inference of context-free grammars using positive and negative language examples is among the most challenging task in modern artificial and natural language technology. Recently, several implementations combining various techniques, usually including the Backus–Naur form, have been proposed. In this paper, we explore [...] Read more.
Grammatical inference of context-free grammars using positive and negative language examples is among the most challenging task in modern artificial and natural language technology. Recently, several implementations combining various techniques, usually including the Backus–Naur form, have been proposed. In this paper, we explore a new implementation of grammatical inference using evolution methods focused on the Greibach normal form and exploiting its properties, and also propose new solutions both in the evolutionary processes and in the corresponding fitness estimation. Full article
(This article belongs to the Special Issue Signal Processing, Grammatical Evolution and Artificial Intelligence of Signals)
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22 pages, 809 KiB  
Article
GRAPE: Grammatical Algorithms in Python for Evolution
by Allan de Lima, Samuel Carvalho, Douglas Mota Dias, Enrique Naredo, Joseph P. Sullivan and Conor Ryan
Signals 2022, 3(3), 642-663; https://doi.org/10.3390/signals3030039 - 15 Sep 2022
Cited by 6 | Viewed by 2636
Abstract
GRAPE is an implementation of Grammatical Evolution (GE) in DEAP, an Evolutionary Computation framework in Python, which consists of the necessary classes and functions to evolve a population of grammar-based solutions, while reporting essential measures. This tool was developed at the Bio-computing and [...] Read more.
GRAPE is an implementation of Grammatical Evolution (GE) in DEAP, an Evolutionary Computation framework in Python, which consists of the necessary classes and functions to evolve a population of grammar-based solutions, while reporting essential measures. This tool was developed at the Bio-computing and Developmental Systems (BDS) Research Group, the birthplace of GE, as an easy to use (compared to the canonical C++ implementation, libGE) tool that inherits all the advantages of DEAP, such as selection methods, parallelism and multiple search techniques, all of which can be used with GRAPE. In this paper, we address some problems to exemplify the use of GRAPE and to perform a comparison with PonyGE2, an existing implementation of GE in Python. The results show that GRAPE has a similar performance, but is able to avail of all the extra facilities and functionality found in the DEAP framework. We further show that GRAPE enables GE to be applied to systems identification problems and we demonstrate this on two benchmark problems. Full article
(This article belongs to the Special Issue Signal Processing, Grammatical Evolution and Artificial Intelligence of Signals)
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14 pages, 1896 KiB  
Article
Applying and Comparing LSTM and ARIMA to Predict CO Levels for a Time-Series Measurements in a Port Area
by Evangelos D. Spyrou, Ioannis Tsoulos and Chrysostomos Stylios
Signals 2022, 3(2), 235-248; https://doi.org/10.3390/signals3020015 - 15 Apr 2022
Cited by 9 | Viewed by 2853
Abstract
Air pollution is a major problem in the everyday life of citizens, especially air pollution in the transport domain. Ships play a significant role in coastal air pollution, in conjunction with transport mobility in the broader area of ports. As such, ports should [...] Read more.
Air pollution is a major problem in the everyday life of citizens, especially air pollution in the transport domain. Ships play a significant role in coastal air pollution, in conjunction with transport mobility in the broader area of ports. As such, ports should be monitored in order to assess air pollution levels and act accordingly. In this paper, we obtain CO values from environmental sensors that were installed in the broader area of the port of Igoumenitsa in Greece. Initially, we analysed the CO values and we have identified some extreme values in the dataset that showed a potential event. Thereafter, we separated the dataset into 6-h intervals and showed that we have an extremely high rise in certain hours. We transformed the dataset to a moving average dataset, with the objective being the reduction of the extremely high values. We utilised a machine-learning algorithm, namely the univariate long short-term memory (LSTM) algorithm to provide the predicted outcome of the time series from the port that has been collected. We performed experiments by using 100, 1000, and 7000 batches of data. We provided results on the model loss and the root-mean-square error as well as the mean absolute error. We showed that with the case with batch number equals to 7000, the LSTM we achieved a good prediction outcome. The proposed method was compared with the ARIMA model and the comparison results prove the merit of the approach. Full article
(This article belongs to the Special Issue Signal Processing, Grammatical Evolution and Artificial Intelligence of Signals)
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15 pages, 454 KiB  
Article
Constructing Features Using a Hybrid Genetic Algorithm
by Ioannis G. Tsoulos
Signals 2022, 3(2), 174-188; https://doi.org/10.3390/signals3020012 - 06 Apr 2022
Viewed by 1885
Abstract
A hybrid procedure that incorporates grammatical evolution and a weight decaying technique is proposed here for various classification and regression problems. The proposed method has two main phases: the creation of features and the evaluation of these features. During the first phase, using [...] Read more.
A hybrid procedure that incorporates grammatical evolution and a weight decaying technique is proposed here for various classification and regression problems. The proposed method has two main phases: the creation of features and the evaluation of these features. During the first phase, using grammatical evolution, new features are created as non-linear combinations of the original features of the datasets. In the second phase, based on the characteristics of the first phase, the original dataset is modified and a neural network trained with a genetic algorithm is applied to this dataset. The proposed method was applied to an extremely wide set of datasets from the relevant literature and the experimental results were compared with four other techniques. Full article
(This article belongs to the Special Issue Signal Processing, Grammatical Evolution and Artificial Intelligence of Signals)
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