On Hyper-parameter Tuning for Stochastic Optimization Algorithms
This provides a potentially incremental tool for researchers and practitioners using stochastic algorithms to automate hyper-parameter tuning, though it is domain-specific to optimization.
The paper tackles the problem of tuning hyper-parameters for stochastic optimization algorithms, such as evolutionary algorithms, by proposing a reinforcement learning-based framework that models tuning as a Markov decision process and uses policy gradient algorithms, with experiments showing it does not require much less running time than Bayesian optimization methods.
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.