New Trends in Computational Intelligence and Applications 2023

A special issue of Mathematical and Computational Applications (ISSN 2297-8747).

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 2410

Special Issue Editors


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Guest Editor
DSC, Tecnológico Nacional de México, Instituto Tecnológico de Veracruz, Veracruz 91897, Veracruz, Mexico
Interests: evolutionary algorithms; machine learning; object-oriented programming

Special Issue Information

Dear Colleagues,

This Special Issue will mainly comprise selected papers presented at the 5th Workshop on New Trends in Computational Intelligence and Applications (CIAPP 2023, see https://bi-level.org/ciapp/ for detailed information). Papers considered to be relevant to the scope of the journal and to be of sufficient quality after evaluation by the reviewers will be published free of charge.

The primary topics of this Special Issue are as follows:

  • machine learning
  • data mining
  • statistical learning
  • automatic image processing
  • intelligent agents/multi agent systems
  • evolutionary computing
  • swarm intelligence
  • combinatorial and numerical optimization
  • parallel and distributed computing in computational intelligence

Dr. Efrén Mezura-Montes
Dr. Rafael Rivera-López
Guest Editors

Manuscript Submission Information

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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. Mathematical and Computational Applications 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 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.

Published Papers (1 paper)

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Research

23 pages, 6500 KiB  
Article
M5GP: Parallel Multidimensional Genetic Programming with Multidimensional Populations for Symbolic Regression
by Luis Cárdenas Florido, Leonardo Trujillo, Daniel E. Hernandez and Jose Manuel Muñoz Contreras
Math. Comput. Appl. 2024, 29(2), 25; https://doi.org/10.3390/mca29020025 - 18 Mar 2024
Viewed by 1695
Abstract
Machine learning and artificial intelligence are growing in popularity thanks to their ability to produce models that exhibit unprecedented performance in domains that include computer vision, natural language processing and code generation. However, such models tend to be very large and complex and [...] Read more.
Machine learning and artificial intelligence are growing in popularity thanks to their ability to produce models that exhibit unprecedented performance in domains that include computer vision, natural language processing and code generation. However, such models tend to be very large and complex and impossible to understand using traditional analysis or human scrutiny. Conversely, Symbolic Regression methods attempt to produce models that are relatively small and (potentially) human-readable. In this domain, Genetic Programming (GP) has proven to be a powerful search strategy that achieves state-of-the-art performance. This paper presents a new GP-based feature transformation method called M5GP, which is hybridized with multiple linear regression to produce linear models, implemented to exploit parallel processing on graphical processing units for efficient computation. M5GP is the most recent variant from a family of feature transformation methods (M2GP, M3GP and M4GP) that have proven to be powerful tools for both classification and regression tasks applied to tabular data. The proposed method was evaluated on SRBench v2.0, the current standard benchmarking suite for Symbolic Regression. Results show that M5GP achieves performance that is competitive with the state-of-the-art, achieving a top-three rank on the most difficult subset of black-box problems. Moreover, it achieves the lowest computation time when compared to other GP-based methods that have similar accuracy scores. Full article
(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications 2023)
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