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Mathematics
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Advances in Genetic Programming and Soft Computing
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Advances in Genetic Programming and Soft Computing

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D2: Operations Research and Fuzzy Decision Making".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 7269

Special Issue Editors

Dr. Marko Ðurasević

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia
Interests: artificial intelligence; machine learning; computational intelligence; greedy algorithms; evolutionary computation; particle swarm optimization; genetic algorithms; fuzzy logic; artificial neural networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Domagoj Jakobović

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia
Interests: genetic programming (GP); evolutionary algorithms; scheduling; soft computing, optimization; parallel programming; machine learning;

Special Issue Information

Dear Colleagues,

Recently, Genetic Programming (GP) and related soft computing methods have gained significant attention from the research community. They have been used to solve various real-world problems, such as problems related to scheduling, transportation, electrical engineering, and many other domains. The reason for their popularity lies in their flexibility and ability to obtain high-quality solutions to complex machine learning and optimization problems in a reasonable amount of time. Recent trends in this area include the study of multi-objective optimization, the application of hyper-heuristics for various combinatorial optimization problems, and solving large-scale and difficult real-world problems. Due to the wide range of applications, as well as the recent advances made in the field of genetic programming and soft computing, it is expected that this area of research will continue to attract the attention of the research community and lead to new significant advances. 

This Special Issue provides an opportunity for researchers to publish their new work in the field of genetic programming and soft computing. Relevant research should apply solution approaches that incorporate genetic programming and metaheuristics (evolutionary computation, local search methods) as well as soft computing techniques (neural networks, fuzzy systems). Submissions dealing with novel algorithms, improvements or hybridization of existing methods, application of relevant methods to various problems, and surveys of existing literature are welcome.

Dr. Marko Ðurasević
Prof. Dr. Domagoj Jakobović
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 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. Mathematics 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 2600 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
  • hyper-heuristics
  • machine learning
  • optimization
  • soft computing
  • metaheuristics
  • neural networks
  • fuzzy systems
  • combinatorial optimization problems

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Published Papers (3 papers)

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Research

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40 pages, 23417 KiB  
Article
MTS-PRO2SAT: Hybrid Mutation Tabu Search Algorithm in Optimizing Probabilistic 2 Satisfiability in Discrete Hopfield Neural Network
by Ju Chen, Yuan Gao, Mohd Shareduwan Mohd Kasihmuddin, Chengfeng Zheng, Nurul Atiqah Romli, Mohd. Asyraf Mansor, Nur Ezlin Zamri and Chuanbiao When
Mathematics 2024, 12(5), 721; https://doi.org/10.3390/math12050721 - 29 Feb 2024
Cited by 8 | Viewed by 1509
Abstract
The primary objective of introducing metaheuristic algorithms into traditional systematic logic is to minimize the cost function. However, there is a lack of research on the impact of introducing metaheuristic algorithms on the cost function under different proportions of positive literals. In order [...] Read more.
The primary objective of introducing metaheuristic algorithms into traditional systematic logic is to minimize the cost function. However, there is a lack of research on the impact of introducing metaheuristic algorithms on the cost function under different proportions of positive literals. In order to fill in this gap and improve the efficiency of the metaheuristic algorithm in systematic logic, we proposed a metaheuristic algorithm based on mutation tabu search and embedded it in probabilistic satisfiability logic in discrete Hopfield neural networks. Based on the traditional tabu search algorithm, the mutation operators of the genetic algorithm were combined to improve its global search ability during the learning phase and ensure that the cost function of the systematic logic converged to zero at different proportions of positive literals. Additionally, further optimization was carried out in the retrieval phase to enhance the diversity of solutions. Compared with nine other metaheuristic algorithms and exhaustive search algorithms, the proposed algorithm was superior to other algorithms in terms of time complexity and global convergence, and showed higher efficiency in the search solutions at the binary search space, consolidated the efficiency of systematic logic in the learning phase, and significantly improved the diversity of the global solution in the retrieval phase of systematic logic. Full article
(This article belongs to the Special Issue Advances in Genetic Programming and Soft Computing)
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22 pages, 354 KiB  
Article
A Variable Neighborhood Search Method with a Tabu List and Local Search for Optimizing Routing in Trucks in Maritime Ports
by Luka Matijević, Marko Đurasević and Domagoj Jakobović
Mathematics 2023, 11(17), 3740; https://doi.org/10.3390/math11173740 - 30 Aug 2023
Viewed by 1424
Abstract
Logistics problems represent an important class of real-world problems where even small improvements in solution quality can lead to significant decreases in operational costs. However, these problems are usually NP-hard; thus, they are mostly solved using metaheuristic methods. To improve their performance, there [...] Read more.
Logistics problems represent an important class of real-world problems where even small improvements in solution quality can lead to significant decreases in operational costs. However, these problems are usually NP-hard; thus, they are mostly solved using metaheuristic methods. To improve their performance, there is substantial research on crafting new and refined metaheuristics to derive superior solutions. This paper considers a truck routing problem within a naval port, where the objective is to minimize the total distance traveled by all the vehicles to distribute a given set of containers. Due to the large volume of goods that are being transferred through ports, it is imperative to improve the operation times at such ports to improve the throughput. To achieve this goal, a novel variable neighborhood search method that integrates a tabu list, an iterative local search procedure, and parallelization of neighborhood generation is proposed and evaluated. The experimental results demonstrate that the proposed method achieves similar results to the state of the art, but in a smaller amount of time. Full article
(This article belongs to the Special Issue Advances in Genetic Programming and Soft Computing)
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Review

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44 pages, 12556 KiB  
Review
Review of Stochastic Dynamic Vehicle Routing in the Evolving Urban Logistics Environment
by Nikola Mardešić, Tomislav Erdelić, Tonči Carić and Marko Đurasević
Mathematics 2024, 12(1), 28; https://doi.org/10.3390/math12010028 - 21 Dec 2023
Cited by 5 | Viewed by 3861
Abstract
Urban logistics encompass transportation and delivery operations within densely populated urban areas. It faces significant challenges from the evolving dynamic and stochastic nature of on-demand and conventional logistics services. Further challenges arise with application doctrines shifting towards crowd-sourced platforms. As a result, “traditional” [...] Read more.
Urban logistics encompass transportation and delivery operations within densely populated urban areas. It faces significant challenges from the evolving dynamic and stochastic nature of on-demand and conventional logistics services. Further challenges arise with application doctrines shifting towards crowd-sourced platforms. As a result, “traditional” deterministic approaches do not adequately fulfil constantly evolving customer expectations. To maintain competitiveness, logistic service providers must adopt proactive and anticipatory systems that dynamically model and evaluate probable (future) events, i.e., stochastic information. These events manifest in problem characteristics such as customer requests, demands, travel times, parking availability, etc. The Stochastic Dynamic Vehicle Routing Problem (SDVRP) addresses the dynamic and stochastic information inherent in urban logistics. This paper aims to analyse the key concepts, challenges, and recent advancements and opportunities in the evolving urban logistics landscape and assess the evolution from classical VRPs, via DVRPs, to state-of-art SDVRPs. Further, coupled with non-reactive techniques, this paper provides an in-depth overview of cutting-edge model-based and model-free reactive solution approaches. Although potent, these approaches become restrictive due to the “curse of dimensionality”. Sacrificing granularity for scalability, researchers have opted for aggregation and decomposition techniques to overcome this problem and recent approaches explore solutions using deep learning. In the scope of this research, we observed that addressing real-world SDVRPs with a comprehensive resolution encounters a set of challenges, emphasising a substantial gap in the research field that warrants further exploration. Full article
(This article belongs to the Special Issue Advances in Genetic Programming and Soft Computing)
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