  For various optimization methods, gradient descent-based algorithms can
achieve outstanding performance and have been widely used in various tasks.
Among those commonly used algorithms, ADAM owns many advantages such as fast
convergence with both the momentum term and the adaptive learning rate.
However, since the loss functions of most deep neural networks are non-convex,
ADAM also shares the drawback of getting stuck in local optima easily. To
resolve such a problem, the idea of combining genetic algorithm with base
learners is introduced to rediscover the best solutions. Nonetheless, from our
analysis, the idea of combining genetic algorithm with a batch of base learners
still has its shortcomings. The effectiveness of genetic algorithm can hardly
be guaranteed if the unit models converge to close or the same solutions. To
resolve this problem and further maximize the advantages of genetic algorithm
with base learners, we propose to implement the boosting strategy for input
model training, which can subsequently improve the effectiveness of genetic
algorithm. In this paper, we introduce a novel optimization algorithm, namely
Boosting based Genetic ADAM (BGADAM). With both theoretic analysis and
empirical experiments, we will show that adding the boosting strategy into the
BGADAM model can help models jump out the local optima and converge to better
solutions.
