Comparison of standard GEP and DGEP.

<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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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Kejia Liu (5699651) (author)
مؤلفون آخرون:	Yiping Teng (21464646) (author), Fang Liu (13271) (author)
منشور في:	2025
الموضوعات:	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 xlink "> 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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Comparison of standard GEP and DGEP.

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