Fitness comparison on test function.

Fitness comparison on test function.

<div><p>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 fro...

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Bibliographic Details
Main Author: Kejia Liu (5699651) (author)
Other Authors: Yiping Teng (21464646) (author), Fang Liu (13271) (author)
Published: 2025
Subjects:
Molecular Biology
Evolutionary Biology
Ecology
Biological Sciences not elsewhere classified
two unique operators
successfully handling high
preventing premature convergence
gene expression programming
complex optimization problems
assess fitness outcomes
artificial intelligence together
alternative improved variants
8 benchmark functions
restrictions block gep
enhance gep performance
larger population diversity
increased population diversity
r &# 178
dgep model serves
maintaining population diversity
population diversification
maintaining exploration
preserve diversity
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scores along
results indicate
produced 15
optimal results
local optima
fast developments
extensive evaluation
exploitation balance
evolutionary search
evolutionary algorithms
escape rate
effective maintenance
dynamically adjusting
dimensional along
dgep produced
dgep occurred
approach operates
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