PAIR: A Novel Large Language Model-Guided Selection Strategy for Evolutionary Algorithms
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909525441773568 |
|---|---|
| author | Ali, Shady Ashraf, Mahmoud Hegazy, Seif Salem, Fatty Mokhtar, Hoda Gaber, Mohamed Medhat Alrefaie, Mohamed Taher |
| author_facet | Ali, Shady Ashraf, Mahmoud Hegazy, Seif Salem, Fatty Mokhtar, Hoda Gaber, Mohamed Medhat Alrefaie, Mohamed Taher |
| contents | Evolutionary Algorithms (EAs) employ random or simplistic selection methods, limiting their exploration of solution spaces and convergence to optimal solutions. The randomness in performing crossover or mutations may limit the model's ability to evolve efficiently. This paper introduces Preference-Aligned Individual Reciprocity (PAIR), a novel selection approach leveraging Large Language Models to emulate human-like mate selection, thereby introducing intelligence to the pairing process in EAs. PAIR prompts an LLM to evaluate individuals within a population based on genetic diversity, fitness level, and crossover compatibility, guiding more informed pairing decisions. We evaluated PAIR against a baseline method called LLM-driven EA (LMEA), published recently. Results indicate that PAIR significantly outperforms LMEA across various TSP instances, achieving lower optimality gaps and improved convergence. This performance is especially noticeable when combined with the flash thinking model, demonstrating increased population diversity to escape local optima. In general, PAIR provides a new strategy in the area of in-context learning for LLM-driven selection in EAs via sophisticated preference modelling, paving the way for improved solutions and further studies into LLM-guided optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03239 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PAIR: A Novel Large Language Model-Guided Selection Strategy for Evolutionary Algorithms Ali, Shady Ashraf, Mahmoud Hegazy, Seif Salem, Fatty Mokhtar, Hoda Gaber, Mohamed Medhat Alrefaie, Mohamed Taher Neural and Evolutionary Computing Machine Learning Evolutionary Algorithms (EAs) employ random or simplistic selection methods, limiting their exploration of solution spaces and convergence to optimal solutions. The randomness in performing crossover or mutations may limit the model's ability to evolve efficiently. This paper introduces Preference-Aligned Individual Reciprocity (PAIR), a novel selection approach leveraging Large Language Models to emulate human-like mate selection, thereby introducing intelligence to the pairing process in EAs. PAIR prompts an LLM to evaluate individuals within a population based on genetic diversity, fitness level, and crossover compatibility, guiding more informed pairing decisions. We evaluated PAIR against a baseline method called LLM-driven EA (LMEA), published recently. Results indicate that PAIR significantly outperforms LMEA across various TSP instances, achieving lower optimality gaps and improved convergence. This performance is especially noticeable when combined with the flash thinking model, demonstrating increased population diversity to escape local optima. In general, PAIR provides a new strategy in the area of in-context learning for LLM-driven selection in EAs via sophisticated preference modelling, paving the way for improved solutions and further studies into LLM-guided optimization. |
| title | PAIR: A Novel Large Language Model-Guided Selection Strategy for Evolutionary Algorithms |
| topic | Neural and Evolutionary Computing Machine Learning |
| url | https://arxiv.org/abs/2503.03239 |