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Genetic Optimization Algorithm in Mathematics
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Genetic Optimization Algorithm in Mathematics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 1166

Special Issue Editors

Dr. Matevž Kunaver

E-Mail Website
Guest Editor
Faculty of Electrical Engineering, University of Ljubljana, 1000 Ljubljana, Slovenia
Interests: computer science; optimization algorithms; recommender systems; evolutionary computation
Prof. Dr. Árpád Bűrmen

E-Mail Website
Guest Editor
Faculty of Electrical Engineering, University of Ljubljana, 1000 Ljubljana, Slovenia
Interests: optimization algorithms; derivative-free optimization; electronic circuit design automation; electronic circuit simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Optimization has become an integral part of many fields of science. It can be used to simplify procedures such as machine learning, network management, prediction and neural networks.

One field of optimization that is rapidly developing is evolutionary computation, which is inspired by nature. The field offers a lot of diversity in topics, such as genetic algorithms, genetic programming and grammatical evolution, to name a few.

Evolutionary computation has the advantage of allowing us to create a “black box” that allows others to optimize their problems without the need for expert knowledge. In addition, it can also sometimes create completely unexpected solutions that can open new possibilities (for example, MIT’s discovery of completely new antibiotics that can combat new drug-resistant strains).

The downside of this approach lies in finding the correct building blocks for each problem. Setting the constraints too wide can result in very slow progress towards the best result. Being too strict, on the other hand, may result in the algorithm missing some of the potential results. Another vital challenge is selecting the correct criteria function in order to find the relevant results. The criteria function is often the key factor in either missing a solution completely or finding a new, optimal solution.

You are therefore cordially invited to submit papers related to all aspects of evolutionary computation, both theoretical and applicational. This involves (but is not limited to) evolutionary algorithms, genetic programming, and grammatical evolution.

Dr. Matevž Kunaver
Prof. Dr. Árpád Bűrmen
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

  • evolutionary computation
  • genetic programming
  • grammatical evolution
  • optimization

Published Papers (1 paper)

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Research

21 pages, 4466 KiB  
Article
An Efficient Evolution-Based Technique for Moving Target Search with Unmanned Aircraft Vehicle: Analysis and Validation
by Mohamed Abdel-Basset, Reda Mohamed, Ibrahim M. Hezam, Ahmad M. Alshamrani and Karam M. Sallam
Mathematics 2023, 11(12), 2606; https://doi.org/10.3390/math11122606 - 7 Jun 2023
Cited by 1 | Viewed by 873
Abstract
Recent advances in technology have led to a surge in interest in unmanned aerial vehicles (UAVs), which are remote-controlled aircraft that rely on cameras or sensors to gather information about their surroundings during flight. A UAV requires a path-planning technique that can swiftly [...] Read more.
Recent advances in technology have led to a surge in interest in unmanned aerial vehicles (UAVs), which are remote-controlled aircraft that rely on cameras or sensors to gather information about their surroundings during flight. A UAV requires a path-planning technique that can swiftly recalculate a viable and quasi-optimal path in flight if a new obstacle or hazard is recognized or if the target is moved during the mission. In brief, the planning of UAV routes might optimize a specific problem determined by the application, such as the moving target problem (MTP), flight time and threats, or multiobjective navigation. The complexity of MTP ranges from NP-hard to NEXP-complete because there are so many probabilistic variables involved. Therefore, it is hard to detect a high-quality solution for this problem using traditional techniques such as differential calculus. Therefore, this paper hybridizes differential evolution (DE) with two newly proposed updating schemes to present a new evolution-based technique named hybrid differential evolution (HDE) for accurately tackling the MTP in a reasonable amount of time. Using Bayesian theory, the MTP can be transformed into an optimization problem by employing the target detection probability as the fitness function. The proposed HDE encodes the search trajectory as a sequence of UAV motion pathways that evolve with increasing the current iteration for finding the near-optimal solution, which could maximize this fitness function. The HDE is extensively compared to the classical DE and several rival optimizers in terms of several performance metrics across four different scenarios with varying degrees of difficulty. This comparison demonstrates the proposal’s superiority in terms of the majority of used performance metrics. Full article
(This article belongs to the Special Issue Genetic Optimization Algorithm in Mathematics)
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