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LLMs show promise in generating genetic programming operators

By PulseAugur Editorial · [1 sources] ·

Researchers have evaluated the ability of eight large language models (LLMs) to generate effective parent-selection operators for genetic programming (GP) in symbolic regression tasks. The study found that models like Claude Sonnet 4.6 and Gemini 3.1 Pro consistently performed well, with one operator synthesized by Kimi K2.5 outperforming standard automatic selection methods. These findings suggest that LLMs can be used in a zero-shot manner to create competitive GP selection operators, often by leveraging semantic understanding to guide the search process. AI

IMPACT Demonstrates LLMs' potential for zero-shot synthesis of specialized operators in computational tasks like genetic programming.

RANK_REASON Academic paper detailing a novel application of LLMs to a specific computational task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs show promise in generating genetic programming operators

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Academic paper detailing a novel application of LLMs to a specific computational task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Mengjie Zhang ·

    Benchmarking Zero-Shot LLM-Generated Parent Selection in Genetic Programming for Symbolic Regression

    Parent selection significantly affects exploration, exploitation, and complexity control in genetic programming (GP) for symbolic regression. It is unclear whether large language models (LLMs) can synthesize effective operators in a zero-shot setting without iterative meta-evolut…

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