Local Search-based Approach for Cost-effective Job Assignment on Large Language Models-Reference-Cited by-同舟云学术

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Local Search-based Approach for Cost-effective Job Assignment on Large Language Models

Published:2024-07-14 Issue: Volume: Page:719-722
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference Companion
language:
Short-container-title:

Author:

Liu Yueyue¹^ORCID,Zhang Hongyu²^ORCID,Le Van-Hoang¹^ORCID,Miao Yuantian¹^ORCID,Li Zhiqiang³^ORCID

Affiliation:

1. The University of Newcastle, Newcastle, Australia

2. Chongqing University, Chongqing, China

3. Shaanxi Normal University, Shaanxi, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3638530.3654104

Reference17 articles.

1. 2015. Welcome to PyGMO. Retrieved April 20, 2024 from https://esa.github.io/pygmo/index.html

2. Charles Audet, Jean Bigeon, Dominique Cartier, Sébastien Le Digabel, and Ludovic Salomon. 2021. Performance indicators in multiobjective optimization. European journal of operational research 292, 2 (2021), 397--422.

3. Pymoo: Multi-Objective Optimization in Python

4. Lingjiao Chen, Matei Zaharia, and James Zou. 2023. FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance. arXiv preprint arXiv:2305.05176 (2023).

5. Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation 6, 2 (2002), 182--197.

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