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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Bibliographic Details
Published in:PeerJ. Computer science Vol. 10; p. e1773
Main Authors: Bucheli, Víctor A., Solarte Pabón, Oswaldo, Ordoñez, Hugo
Format: Journal Article
Language:English
Published: United States PeerJ. Ltd 09.01.2024
PeerJ, Inc
PeerJ Inc
Subjects:
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
Optimization problems
Complex networks
Evolutionary algorithms
ISSN:2376-5992, 2376-5992
Online Access:Get full text
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Summary: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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ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.1773