Applications of Evolutionary Algorithms

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

Deadline for manuscript submissions: closed (1 August 2021) | Viewed by 4253

Special Issue Editors

Department of Software Engineering, University of Granada, C/Periodista Daniel Saucedo Aranda, sn, 18071 Granada, Spain
Interests: evolutionary computation; distributed algorithms; service oriented architecture; video games
French National Institute for Agriculture, Food, and Environment (INRAE), 75338 Paris, France
Interests: real-world application of evolutionary computation and stochastic optimization to problems; especially on semi-supervised modeling of food processes

Special Issue Information

Dear Colleagues,

Evolutionary algorithms, such as genetic algorithms or genetic programming, have made great progress in various areas. Their ease of application in real problems, along with the possibility of hybridisation with other techniques, has meant that their use has extended beyond the theoretical field, having obtained a large number of improvements in very different fields, such as economy, biology, medicine, or industry. This Special Issue is intended to be a common ground for the evolutionary algorithm community, and researchers using them in different fields are invited to share their latest findings.

Topics include the application of evolutionary algorithms to the next areas:

  • Real-World Industrial and Commercial Environments
  • Business Analytics and Finance
  • Networks and other Parallel and Distributed Systems.
  • Stochastic and Dynamic Environments
  • Social Networks and Complex Systems
  • Communication Networks and other Parallel and Distributed Systems
  • Image Analysis, Signal Processing and Pattern Recognition
  • Energy Applications
  • Software Engineering and Testing
  • Biology and Medicine
  • Art and Design
  • Games
  • Robotics
  • Other

Dr. Pablo García-Sánchez
Dr. Alberto Paolo Tonda
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. 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 Algorithms
  • Genetic Programming
  • Genetic Algorithms
  • Art
  • Health
  • Games
  • Business and Finance
  • Complex Systems
  • Business and Finance
  • Networks
  • Biology

Published Papers (2 papers)

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Research

18 pages, 1564 KiB  
Article
Genetic Hybrid Optimization of a Real Bike Sharing System
by Gonzalo A. Aranda-Corral, Miguel A. Rodríguez, Iñaki Fernández de Viana and María Isabel G. Arenas
Mathematics 2021, 9(18), 2227; https://0-doi-org.brum.beds.ac.uk/10.3390/math9182227 - 10 Sep 2021
Cited by 1 | Viewed by 1679
Abstract
In recent years there has been a growing interest in resource sharing systems as one of the possible ways to support sustainability. The use of resource pools, where people can drop a resource to be used by others in a local context, is [...] Read more.
In recent years there has been a growing interest in resource sharing systems as one of the possible ways to support sustainability. The use of resource pools, where people can drop a resource to be used by others in a local context, is highly dependent on the distribution of those resources on a map or graph. The optimization of these systems is an NP-Hard problem given its combinatorial nature and the inherent computational load required to simulate the use of a system. Furthermore, it is difficult to determine system overhead or unused resources without building the real system and test it in real conditions. Nevertheless, algorithms based on a candidate solution allow measuring hypothetical situations without the inconvenience of a physical implementation. In particular, this work focuses on obtaining the past usage of bike loan network infrastructures to optimize the station’s capacity distribution. Bike sharing systems are a good model for resource sharing systems since they contain common characteristics, such as capacity, distance, and temporary restrictions, which are present in most geographically distributed resources systems. To achieve this target, we propose a new approach based on evolutionary algorithms whose evaluation function will consider the cost of non-used bike places as well as the additional kilometers users would have to travel in the new distribution. To estimate its value, we will consider the geographical proximity and the trend in the areas to infer the behavior of users. This approach, which improves user satisfaction considering the past usage of the former infrastructure, as far as we know, has not been applied to this type of problem and can be generalized to other resource sharing problems with usage data. Full article
(This article belongs to the Special Issue Applications of Evolutionary Algorithms)
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22 pages, 6193 KiB  
Article
Using an Improved Differential Evolution for Scheduling Optimization of Dual-Gantry Multi-Head Surface-Mount Placement Machine
by Cheng-Jian Lin and Chun-Hui Lin
Mathematics 2021, 9(16), 2016; https://0-doi-org.brum.beds.ac.uk/10.3390/math9162016 - 23 Aug 2021
Cited by 3 | Viewed by 1770
Abstract
The difference between dual-gantry and single-gantry surface-mount placement (SMP) machines is that dual-gantry machines exhibit higher complexity and more problems due to their additional gantry robot, such as component allocation and collision. This paper presents algorithms to prescribe the assembly operations of a [...] Read more.
The difference between dual-gantry and single-gantry surface-mount placement (SMP) machines is that dual-gantry machines exhibit higher complexity and more problems due to their additional gantry robot, such as component allocation and collision. This paper presents algorithms to prescribe the assembly operations of a dual-gantry multi-head surface-mount placement machine. It considers five inter-related problems: (i) component allocation; (ii) automatic nozzle changer assignment; (iii) feeder arrangement; and (iv) pick-and-place sequence; it incorporates a practical restriction related to (v) component height. The paper proposes a solution to each problem: (i) equalizing “workloads” assigned to the gantries, (ii) using quantity ratio method, (iii) using two similarity measurement mechanisms in a modified differential evolution algorithm with a random-key encoding mapping method that addresses component height restriction, (iv) and a combination of nearest-neighbor search and 2-opt method to plan each placing operation. This study reports an experiment that involved the processing of 10 printed circuit boards and compared the performance of a modified differential evolution algorithm with well-known algorithms including differential evolution, particle swarm optimization, and genetic algorithm. The results reveal that the number of picks, moving distance of picking components, and total assembly time with the modified differential evolution algorithm are less than other algorithms. Full article
(This article belongs to the Special Issue Applications of Evolutionary Algorithms)
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