This article proposes a Universal Activation Function (UAF) that achieves
near optimal performance in quantification, classification, and reinforcement
learning (RL) problems. For any given problem, the optimization algorithms are
able to evolve the UAF to a suitable activation function by tuning the UAF's
parameters. For the CIFAR-10 classification and VGG-8, the UAF converges to the
Mish like activation function, which has near optimal performance F1​=0.9017±0.0040 when compared to other activation functions. For the
quantification of simulated 9-gas mixtures in 30 dB signal-to-noise ratio (SNR)
environments, the UAF converges to the identity function, which has near
optimal root mean square error of 0.4888±0.0032μM. In the
BipedalWalker-v2 RL dataset, the UAF achieves the 250 reward in 961±193
epochs, which proves that the UAF converges in the lowest number of epochs.
Furthermore, the UAF converges to a new activation function in the
BipedalWalker-v2 RL dataset