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Genetic Programming, Theory, Methods and Applications
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Genetic Programming, 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 (20 September 2022) | Viewed by 16392

Special Issue Editors

Dr. Mauro Castelli

E-Mail Website
Guest Editor
NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, 1070-312 Lisboa, Portugal
Interests: genetic programming; evolutionary computation; machine learning; neuroevolution; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Luca Manzoni

E-Mail Website
Guest Editor
Department of Mathematics and Geosciences, University of Trieste, 34127 Trieste, Italy
Interests: genetic programming; evolutionary computation; bioinspired computational models; theoretical computer science; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

   Genetic Programming (GP) is the most recent technique in the field of evolutionary computation. It is inspired by the principles of Darwinian evolution, and aims to automatically build computer programs for accomplishing specific tasks. Recent years have seen several successful applications of GP for addressing complex real-world problems in various domains. In addition to the applications of GP, researchers have put significant effort into the theoretical study of GP. Important results have been obtained in the definition of semantics-based operators and the run-time analysis of GP. With the growing popularity of Deep Learning (DL), evolutionary computation research is now focused on the definition of suitable methods for evolving the topology of a DL architecture. This topic has been studied previously, especially with regard to shallow architectures. Additionally, hybridization of GP with other machine learning techniques may provide new results from both theoretical and practical points of view.

    This Special Issue aims to collect contributions in the area of GP with the goal of advancing the current knowledge of the field. In particular, contributions covering theoretical contributions are encouraged. These contributions will ideally be focused on hybridization methods, neuroevolution, semantics, run-time analysis, or other significant topics in the field of GP. Practical contributions are also welcome, but they should report significant results achieved with GP to overcome complex real-world problems.

Dr. Mauro Castelli
Dr. Luca Manzoni
Guest Editors

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Keywords

  • genetic programming
  • evolutionary computation
  • hybridization
  • semantics
  • neuroevolution.

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Published Papers (5 papers)

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Research

14 pages, 3613 KiB  
Article
Stress Analysis of 2D-FG Rectangular Plates with Multi-Gene Genetic Programming
by Munise Didem Demirbas, Didem Çakır, Celal Ozturk and Sibel Arslan
Appl. Sci. 2022, 12(16), 8198; https://doi.org/10.3390/app12168198 - 16 Aug 2022
Cited by 3 | Viewed by 1909
Abstract
Functionally Graded Materials (FGMs) are designed for use in high-temperature applications. Since the mass production of FGM has not yet been made, the determination of its thermo-mechanical limits depends on the compositional gradient exponent value. In this study, an efficient working model is [...] Read more.
Functionally Graded Materials (FGMs) are designed for use in high-temperature applications. Since the mass production of FGM has not yet been made, the determination of its thermo-mechanical limits depends on the compositional gradient exponent value. In this study, an efficient working model is created for the thermal stress problem of the 2D-FG plate using Multi-gene Genetic Programming (MGGP). In our MGGP model in this study, data sets obtained from the numerical analysis results of the thermal stress problem are used, and formulas that give equivalent stress levels as output data, with the input data being the compositional gradient exponent, are obtained. For the current problem, efficient models that reduce CPU processing time are obtained by using the MGGP method. Full article
(This article belongs to the Special Issue Genetic Programming, Theory, Methods and Applications)
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25 pages, 769 KiB  
Article
An Evolutionary Computation Approach for Twitter Bot Detection
by Luigi Rovito, Lorenzo Bonin, Luca Manzoni and Andrea De Lorenzo
Appl. Sci. 2022, 12(12), 5915; https://doi.org/10.3390/app12125915 - 10 Jun 2022
Cited by 4 | Viewed by 2643
Abstract
Bot accounts are automated software programs that act as legitimate human profiles on social networks. Identifying these kinds of accounts is a challenging problem due to the high variety and heterogeneity that bot accounts exhibit. In this work, we use genetic algorithms and [...] Read more.
Bot accounts are automated software programs that act as legitimate human profiles on social networks. Identifying these kinds of accounts is a challenging problem due to the high variety and heterogeneity that bot accounts exhibit. In this work, we use genetic algorithms and genetic programming to discover interpretable classification models for Twitter bot detection with competitive qualitative performance, high scalability, and good generalization capabilities. Specifically, we use a genetic programming method with a set of primitives that involves simple mathematical operators. This enables us to discover a human-readable detection algorithm that exhibits a detection accuracy close to the top state-of-the-art methods on the TwiBot-20 dataset while providing predictions that can be interpreted, and whose uncertainty can be easily measured. To the best of our knowledge, this work is the first attempt at adopting evolutionary computation techniques for detecting bot profiles on social media platforms. Full article
(This article belongs to the Special Issue Genetic Programming, Theory, Methods and Applications)
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21 pages, 15593 KiB  
Article
Towards Automatic Image Enhancement with Genetic Programming and Machine Learning
by João Correia, Nereida Rodriguez-Fernandez, Leonardo Vieira, Juan Romero and Penousal Machado
Appl. Sci. 2022, 12(4), 2212; https://doi.org/10.3390/app12042212 - 20 Feb 2022
Cited by 5 | Viewed by 2819
Abstract
Image Enhancement (IE) is an image processing procedure in which the image’s original information is improved, highlighting specific features to ease post-processing analyses by a human or machine. State-of-the-art image enhancement pipelines apply solutions to fixed and static constraints to solve specific issues [...] Read more.
Image Enhancement (IE) is an image processing procedure in which the image’s original information is improved, highlighting specific features to ease post-processing analyses by a human or machine. State-of-the-art image enhancement pipelines apply solutions to fixed and static constraints to solve specific issues in isolation. In this work, an IE system for image marketing is proposed, more precisely, real estate marketing, where the objective is to enhance the commercial appeal of the images, while maintaining a level of realism and similarity with the original image. This work proposes a generic image enhancement pipeline that combines state-of-the-art image processing filters, Machine Learning methods, and Evolutionary approaches, such as Genetic Programming (GP), to create a dynamic framework for Image Enhancement. The GP-based system is trained to optimize 4 metrics: Neural Image Assessment (NIMA) technical and BRISQUE, which evaluate the technical quality of the images; and NIMA aesthetics and PhotoILike, that evaluate the commercial attractiveness. It is shown that the GP model was able to find the best image quality enhancement (0.97 NIMA Aesthetics), while maintaining a high level of similarity with the original images (Structural Similarity Index Measure (SSIM) of 0.88). The framework has better performance according to the image quality metrics than the off-the-shelf image enhancement tool and the framework’s isolated parts. Full article
(This article belongs to the Special Issue Genetic Programming, Theory, Methods and Applications)
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18 pages, 5882 KiB  
Article
Constrained Multi-Objective Optimization of Simulated Tree Pruning with Heterogeneous Criteria
by Damjan Strnad and Štefan Kohek
Appl. Sci. 2021, 11(22), 10781; https://doi.org/10.3390/app112210781 - 15 Nov 2021
Cited by 2 | Viewed by 2061
Abstract
Virtual pruning of simulated fruit tree models is a useful functionality provided by software tools for computer-aided horticultural education and research. It also enables algorithmic pruning optimization with respect to a set of quantitative objectives, which is important for analytical purposes and potential [...] Read more.
Virtual pruning of simulated fruit tree models is a useful functionality provided by software tools for computer-aided horticultural education and research. It also enables algorithmic pruning optimization with respect to a set of quantitative objectives, which is important for analytical purposes and potential applications in automated pruning. However, the existing studies in pruning optimization focus on a single type of objective, such as light distribution within the crown. In this paper, we propose the use of heterogeneous objectives for discrete multi-objective optimization of simulated tree pruning. In particular, the average light intake, crown shape, and tree balance are used to observe the emergence of different pruning patterns in the non-dominated solution sets. We also propose the use of independent constraint objectives as a new mechanism to confine overfitting of solutions to individual pruning criteria. Finally, we perform the comparison of NSGA-II, SPEA2, and MOEA/D-EAM on this task. The results demonstrate that SPEA2 and MOEA/D-EAM, which use external solution archives, can produce better sets of non-dominated solutions than NSGA-II. Full article
(This article belongs to the Special Issue Genetic Programming, Theory, Methods and Applications)
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34 pages, 648 KiB  
Article
General Purpose Optimization Library (GPOL): A Flexible and Efficient Multi-Purpose Optimization Library in Python
by Illya Bakurov, Marco Buzzelli, Mauro Castelli, Leonardo Vanneschi and Raimondo Schettini
Appl. Sci. 2021, 11(11), 4774; https://doi.org/10.3390/app11114774 - 23 May 2021
Cited by 9 | Viewed by 4920
Abstract
Several interesting libraries for optimization have been proposed. Some focus on individual optimization algorithms, or limited sets of them, and others focus on limited sets of problems. Frequently, the implementation of one of them does not precisely follow the formal definition, and they [...] Read more.
Several interesting libraries for optimization have been proposed. Some focus on individual optimization algorithms, or limited sets of them, and others focus on limited sets of problems. Frequently, the implementation of one of them does not precisely follow the formal definition, and they are difficult to personalize and compare. This makes it difficult to perform comparative studies and propose novel approaches. In this paper, we propose to solve these issues with the General Purpose Optimization Library (GPOL): a flexible and efficient multipurpose optimization library that covers a wide range of stochastic iterative search algorithms, through which flexible and modular implementation can allow for solving many different problem types from the fields of continuous and combinatorial optimization and supervised machine learning problem solving. Moreover, the library supports full-batch and mini-batch learning and allows carrying out computations on a CPU or GPU. The package is distributed under an MIT license. Source code, installation instructions, demos and tutorials are publicly available in our code hosting platform (the reference is provided in the Introduction). Full article
(This article belongs to the Special Issue Genetic Programming, Theory, Methods and Applications)
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