Special Issue "Genetic Programming"

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

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editors

Dr. Luca Manzoni
E-Mail Website
Guest Editor
Dipartimento di Matematica e Geoscienze, Università degli Studi di Trieste, 34127 Trieste, Italy
Interests: genetic programming; evolutionary computation; bioinspired computational models; theoretical computer science; machine learning
Special Issues and Collections in MDPI journals
Dr. Andrea De Lorenzo
E-Mail Website
Guest Editor
Department of Engineering and Architecture, University of Trieste, 34127 Trieste, Italy
Interests: cybersecurity; machine learning; information extraction
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to to submit your work to this Special Issue on “Genetic Programming”. We invite the submission of recent state-of-the-art advancements and up-to-date issues in the field of genetic programming (GP), evolutionary computation, and population-based optimization. We welcome paper tackling theoretical studies, experimental investigations, or practical applications.

Potential topics include but are not limited to non-traditional tree-based GP models, like grammatical evolution, geometric semantic GP, linear GP, Cartesian GP, neuroevolutionary approaches, and the hybridization of GP with other evolutionary algorithms or machine learning methods.

Dr. Luca Manzoni
Dr. Andrea De Lorenzo
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 papers will be 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 1400 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

  • Genetic programming
  • Evolutionary computation
  • Semantic methods in evolutionary computation
  • Grammatical evolution
  • Neuroevolution
  • Population-based optimization methods.

Published Papers (2 papers)

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Research

Open AccessArticle
EEG Feature Extraction Using Genetic Programming for the Classification of Mental States
Algorithms 2020, 13(9), 221; https://doi.org/10.3390/a13090221 - 03 Sep 2020
Viewed by 1082
Abstract
The design of efficient electroencephalogram (EEG) classification systems for the detection of mental states is still an open problem. Such systems can be used to provide assistance to humans in tasks where a certain level of alertness is required, like in surgery or [...] Read more.
The design of efficient electroencephalogram (EEG) classification systems for the detection of mental states is still an open problem. Such systems can be used to provide assistance to humans in tasks where a certain level of alertness is required, like in surgery or in the operation of heavy machines, among others. In this work, we extend a previous study where a classification system is proposed using a Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for the classification of two mental states, namely a relaxed and a normal state. Here, we propose an enhanced feature extraction algorithm (Augmented Feature Extraction with Genetic Programming, or +FEGP) that improves upon previous results by employing a Genetic-Programming-based methodology on top of the CSP. The proposed algorithm searches for non-linear transformations that build new features and simplify the classification task. Although the proposed algorithm can be coupled with any classifier, LDA achieves 78.8% accuracy, the best predictive accuracy among tested classifiers, significantly improving upon previously published results on the same real-world dataset. Full article
(This article belongs to the Special Issue Genetic Programming)
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Open AccessArticle
Forecasting Electricity Prices: A Machine Learning Approach
Algorithms 2020, 13(5), 119; https://doi.org/10.3390/a13050119 - 08 May 2020
Viewed by 1945
Abstract
The electricity market is a complex, evolutionary, and dynamic environment. Forecasting electricity prices is an important issue for all electricity market participants. In this study, we shed light on how to improve electricity price forecasting accuracy through the use of a machine learning [...] Read more.
The electricity market is a complex, evolutionary, and dynamic environment. Forecasting electricity prices is an important issue for all electricity market participants. In this study, we shed light on how to improve electricity price forecasting accuracy through the use of a machine learning technique—namely, a novel genetic programming approach. Drawing on empirical data from the largest EU energy markets, we propose a forecasting model that considers variables related to weather conditions, oil prices, and CO2 coupons and predicts energy prices 24 h ahead. We show that the proposed model provides more accurate predictions of future electricity prices than existing prediction methods. Our important findings will assist the electricity market participants in forecasting future price movements. Full article
(This article belongs to the Special Issue Genetic Programming)
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