  The choices of hyperparameters have critical effects on the performance of
machine learning models. In this paper, we present a general framework that is
able to construct an adaptive optimizer, which automatically adjust the
appropriate algorithm and parameters in the process of optimization. Examining
the method of adaptive optimizer, we product an example of using genetic
algorithm to construct an adaptive optimizer based on Bayesian Optimizer and
compared effectiveness with original optimizer. Especially, It has great
advantages in parallel optimization.
