Hyperparameter Adaptive Search for Surrogate Optimization: A Self-Adjusting Approach

• URL: http://arxiv.org/abs/2310.07970v1
• Date: Thu, 12 Oct 2023 01:26:05 GMT
• Title: Hyperparameter Adaptive Search for Surrogate Optimization: A Self-Adjusting Approach
• Authors: Nazanin Nezami and Hadis Anahideh
• Abstract summary: Surrogate optimization (SO) algorithms have shown promise for optimizing expensive black-box functions. Our approach identifies and modifies the most influential hyper parameters specific to each problem and SO approach. Experimental results demonstrate the effectiveness of HASSO in enhancing the performance of various SO algorithms.
• Score: 1.6317061277457001
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Surrogate Optimization (SO) algorithms have shown promise for optimizing expensive black-box functions. However, their performance is heavily influenced by hyperparameters related to sampling and surrogate fitting, which poses a challenge to their widespread adoption. We investigate the impact of hyperparameters on various SO algorithms and propose a Hyperparameter Adaptive Search for SO (HASSO) approach. HASSO is not a hyperparameter tuning algorithm, but a generic self-adjusting SO algorithm that dynamically tunes its own hyperparameters while concurrently optimizing the primary objective function, without requiring additional evaluations. The aim is to improve the accessibility, effectiveness, and convergence speed of SO algorithms for practitioners. Our approach identifies and modifies the most influential hyperparameters specific to each problem and SO approach, reducing the need for manual tuning without significantly increasing the computational burden. Experimental results demonstrate the effectiveness of HASSO in enhancing the performance of various SO algorithms across different global optimization test problems.

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