Evolutionary algorithms guided by Erdős–Rényi complex networks
This article proposes an evolutionary algorithm integrating Erdős–Rényi complex networks to regulate population crossovers, enhancing candidate solution refinement across generations. In this context, the population is conceptualized as a set of interrelated solutions, resembling a complex network....
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| Veröffentlicht in: | PeerJ. Computer science Jg. 10; S. e1773 |
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| Abstract | This article proposes an evolutionary algorithm integrating Erdős–Rényi complex networks to regulate population crossovers, enhancing candidate solution refinement across generations. In this context, the population is conceptualized as a set of interrelated solutions, resembling a complex network. The algorithm enhances solutions by introducing new connections between them, thereby influencing population dynamics and optimizing the problem-solving process. The study conducts experiments comparing four instances of the traditional optimization problem known as the Traveling Salesman Problem (TSP). These experiments employ the traditional evolutionary algorithm, alternative algorithms utilizing different types of complex networks, and the proposed algorithm. The findings suggest that the approach guided by an Erdős–Rényi dynamic network surpasses the performance of the other algorithms. The proposed model exhibits improved convergence rates and shorter execution times. Thus, strategies based on complex networks reveal that network characteristics provide valuable information for solving optimization problems. Therefore, complex networks can regulate the decision-making process, similar to optimizing problems. This work emphasizes that the network structure is crucial in adding value to decision-making. |
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| AbstractList | This article proposes an evolutionary algorithm integrating Erdős-Rényi complex networks to regulate population crossovers, enhancing candidate solution refinement across generations. In this context, the population is conceptualized as a set of interrelated solutions, resembling a complex network. The algorithm enhances solutions by introducing new connections between them, thereby influencing population dynamics and optimizing the problem-solving process. The study conducts experiments comparing four instances of the traditional optimization problem known as the Traveling Salesman Problem (TSP). These experiments employ the traditional evolutionary algorithm, alternative algorithms utilizing different types of complex networks, and the proposed algorithm. The findings suggest that the approach guided by an Erdős-Rényi dynamic network surpasses the performance of the other algorithms. The proposed model exhibits improved convergence rates and shorter execution times. Thus, strategies based on complex networks reveal that network characteristics provide valuable information for solving optimization problems. Therefore, complex networks can regulate the decision-making process, similar to optimizing problems. This work emphasizes that the network structure is crucial in adding value to decision-making. This article proposes an evolutionary algorithm integrating Erdos-Rényi complex networks to regulate population crossovers, enhancing candidate solution refinement across generations. In this context, the population is conceptualized as a set of interrelated solutions, resembling a complex network. The algorithm enhances solutions by introducing new connections between them, thereby influencing population dynamics and optimizing the problem-solving process. The study conducts experiments comparing four instances of the traditional optimization problem known as the Traveling Salesman Problem (TSP). These experiments employ the traditional evolutionary algorithm, alternative algorithms utilizing different types of complex networks, and the proposed algorithm. The findings suggest that the approach guided by an Erdos-Rényi dynamic network surpasses the performance of the other algorithms. The proposed model exhibits improved convergence rates and shorter execution times. Thus, strategies based on complex networks reveal that network characteristics provide valuable information for solving optimization problems. Therefore, complex networks can regulate the decision-making process, similar to optimizing problems. This work emphasizes that the network structure is crucial in adding value to decision-making. This article proposes an evolutionary algorithm integrating Erdős-Rényi complex networks to regulate population crossovers, enhancing candidate solution refinement across generations. In this context, the population is conceptualized as a set of interrelated solutions, resembling a complex network. The algorithm enhances solutions by introducing new connections between them, thereby influencing population dynamics and optimizing the problem-solving process. The study conducts experiments comparing four instances of the traditional optimization problem known as the Traveling Salesman Problem (TSP). These experiments employ the traditional evolutionary algorithm, alternative algorithms utilizing different types of complex networks, and the proposed algorithm. The findings suggest that the approach guided by an Erdős-Rényi dynamic network surpasses the performance of the other algorithms. The proposed model exhibits improved convergence rates and shorter execution times. Thus, strategies based on complex networks reveal that network characteristics provide valuable information for solving optimization problems. Therefore, complex networks can regulate the decision-making process, similar to optimizing problems. This work emphasizes that the network structure is crucial in adding value to decision-making.This article proposes an evolutionary algorithm integrating Erdős-Rényi complex networks to regulate population crossovers, enhancing candidate solution refinement across generations. In this context, the population is conceptualized as a set of interrelated solutions, resembling a complex network. The algorithm enhances solutions by introducing new connections between them, thereby influencing population dynamics and optimizing the problem-solving process. The study conducts experiments comparing four instances of the traditional optimization problem known as the Traveling Salesman Problem (TSP). These experiments employ the traditional evolutionary algorithm, alternative algorithms utilizing different types of complex networks, and the proposed algorithm. The findings suggest that the approach guided by an Erdős-Rényi dynamic network surpasses the performance of the other algorithms. The proposed model exhibits improved convergence rates and shorter execution times. Thus, strategies based on complex networks reveal that network characteristics provide valuable information for solving optimization problems. Therefore, complex networks can regulate the decision-making process, similar to optimizing problems. This work emphasizes that the network structure is crucial in adding value to decision-making. |
| ArticleNumber | e1773 |
| Audience | Academic |
| Author | Solarte Pabón, Oswaldo Bucheli, Víctor A. Ordoñez, Hugo |
| Author_xml | – sequence: 1 givenname: Víctor A. orcidid: 0000-0002-0885-8699 surname: Bucheli fullname: Bucheli, Víctor A. organization: Escuela de Ingeniería de Sistemas y Computación, Universidad del Valle, Cali, Valle, Colombia – sequence: 2 givenname: Oswaldo surname: Solarte Pabón fullname: Solarte Pabón, Oswaldo organization: Escuela de Ingeniería de Sistemas y Computación, Universidad del Valle, Cali, Valle, Colombia – sequence: 3 givenname: Hugo surname: Ordoñez fullname: Ordoñez, Hugo organization: Departamento de Sistemas, Universidad del Cauca, Popayán, Cauca, Colombia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38259892$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.14483/23448350.18039 10.1038/30918 10.1007/BF00127077 10.1007/978-1-84996-129-5 10.1109/TCYB.2014.2305974 10.7551/mitpress/1090.001.0001 10.1016/j.dam.2015.01.035 10.1016/j.asoc.2015.03.038 10.25088/ComplexSystems.20.2.127 10.3233/FI-1998-35123401 10.1016/0377-2217(92)90138-Y 10.1016/j.knosys.2021.107199 10.1016/j.asoc.2018.06.047 10.1016/j.swevo.2019.04.008 10.1016/j.physa.2016.07.050 10.1016/j.mbs.2012.07.002 10.1109/TEVC.2017.2737600 |
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| Copyright | 2024 Bucheli et al. COPYRIGHT 2024 PeerJ. Ltd. 2024 Bucheli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2024 Bucheli et al. 2024 Bucheli et al. |
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| Keywords | Optimization problems Complex networks Evolutionary algorithms |
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| SubjectTerms | Algorithms Algorithms and Analysis of Algorithms Analysis Artificial Intelligence Cities Complex networks Decision making Evolutionary algorithms Genetic algorithms Network Science and Online Social Networks Networks Optimization Optimization problems Optimization techniques Population biology Problem solving Traveling salesman problem |
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| Title | Evolutionary algorithms guided by Erdős–Rényi complex networks |
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