A study of gene expression programming algorithm for dynamically adjusting the parameters of genetic operators

The fast developments in artificial intelligence together with evolutionary algorithms have not solved all the difficulties that Gene Expression Programming (GEP) encounters when maintaining population diversity and preventing premature convergence. Its restrictions block GEP from successfully handl...

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Vydáno v:PloS one Ročník 20; číslo 6; s. e0321711
Hlavní autoři: Liu, Kejia, Teng, Yiping, Liu, Fang
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 02.06.2025
Public Library of Science (PLoS)
Témata:
Ablation
Accuracy
Algorithms
Analysis
Artificial Intelligence
Benchmarks
Biology and Life Sciences
Ecology and Environmental Sciences
Efficiency
Evolutionary algorithms
Exploitation
Gene Expression
Genetic algorithms
Genetic engineering
Humans
Models, Genetic
Mutation
Operators
Optimization
Pareto optimum
Physical Sciences
Population genetics
Programming
Research and Analysis Methods
Searching
China
ISSN:1932-6203, 1932-6203
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Shrnutí:The fast developments in artificial intelligence together with evolutionary algorithms have not solved all the difficulties that Gene Expression Programming (GEP) encounters when maintaining population diversity and preventing premature convergence. Its restrictions block GEP from successfully handling high-dimensional along with complex optimization problems. This research develops Dynamic Gene Expression Programming (DGEP) as an algorithm to control genetic operators dynamically thus achieving improved global search with increased population diversity. The approach operates with two unique operators which include Adaptive Regeneration Operator (DGEP-R) and Dynamically Adjusted Mutation Operator (DGEP-M) to preserve diversity while maintaining exploration-exploitation balance during evolutionary search. An extensive evaluation of DGEP occurred through symbolic regression problem tests. The study employed traditional benchmark functions and conducted evaluations versus baselines Standard GEP, NMO-SARA, and MS-GEP-A to assess fitness outcomes, R² values, population diversification, and the avoidance of local optima. All key metric evaluations showed that DGEP beat standard GEP along with alternative improved variants. DGEP produced the optimal results for 8 benchmark functions that produced 15.7% better R² scores along with 2.3 × larger population diversity. The escape rate from local optima within DGEP reached 35% higher than what standard GEP could achieve. The DGEP model serves to enhance GEP performance through the effective maintenance of diversity and improved global search functions. The results indicate that adaptive genetic methods strengthen evolutionary procedures for solving complex problems effectively.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0321711