On Hyper-parameter Tuning for Stochastic Optimization Algorithms

• URL: http://arxiv.org/abs/2003.02038v2
• Date: Tue, 10 Mar 2020 16:32:23 GMT
• Title: On Hyper-parameter Tuning for Stochastic Optimization Algorithms
• Authors: Haotian Zhang, Jianyong Sun and Zongben Xu
• Abstract summary: This paper proposes the first-ever algorithmic framework for tuning hyper-parameters of optimization algorithm based on reinforcement learning. The proposed framework can be used as a standard tool for hyper-parameter tuning in algorithms.
• Score: 28.88646928299302
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes the first-ever algorithmic framework for tuning hyper-parameters of stochastic optimization algorithm based on reinforcement learning. Hyper-parameters impose significant influences on the performance of stochastic optimization algorithms, such as evolutionary algorithms (EAs) and meta-heuristics. Yet, it is very time-consuming to determine optimal hyper-parameters due to the stochastic nature of these algorithms. We propose to model the tuning procedure as a Markov decision process, and resort the policy gradient algorithm to tune the hyper-parameters. Experiments on tuning stochastic algorithms with different kinds of hyper-parameters (continuous and discrete) for different optimization problems (continuous and discrete) show that the proposed hyper-parameter tuning algorithms do not require much less running times of the stochastic algorithms than bayesian optimization method. The proposed framework can be used as a standard tool for hyper-parameter tuning in stochastic algorithms.

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