OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter Optimization

• URL: http://arxiv.org/abs/2505.19205v2
• Date: Wed, 28 May 2025 15:13:41 GMT
• Title: OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter Optimization
• Authors: Meher Bhaskar Madiraju, Meher Sai Preetam Madiraju,
• Abstract summary: This paper introduces OptiMindTune, a novel multi-agent framework designed to intelligently and efficiently optimize hyperparameters.<n>We leverage the collaborative intelligence of three specialized AI agents -- a Recommender Agent, an Evaluator Agent, and a Decision Agent -- each powered by Google's Gemini models.<n>Our framework integrates principles from advanced large language models, and adaptive search to achieve scalable and intelligent AutoML.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hyperparameter optimization (HPO) is a critical yet challenging aspect of machine learning model development, significantly impacting model performance and generalization. Traditional HPO methods often struggle with high dimensionality, complex interdependencies, and computational expense. This paper introduces OptiMindTune, a novel multi-agent framework designed to intelligently and efficiently optimize hyperparameters. OptiMindTune leverages the collaborative intelligence of three specialized AI agents -- a Recommender Agent, an Evaluator Agent, and a Decision Agent -- each powered by Google's Gemini models. These agents address distinct facets of the HPO problem, from model selection and hyperparameter suggestion to robust evaluation and strategic decision-making. By fostering dynamic interactions and knowledge sharing, OptiMindTune aims to converge to optimal hyperparameter configurations more rapidly and robustly than existing single-agent or monolithic approaches. Our framework integrates principles from advanced large language models, and adaptive search to achieve scalable and intelligent AutoML. We posit that this multi-agent paradigm offers a promising avenue for tackling the increasing complexity of modern machine learning model tuning.

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