Properties for building a Multilayer Perceptron Neural Network using Keras?
1) Activation is an architecture choice, which boils down to a hyperparameter choice. You can make a theoretical argument for using any function, but the best way to determine this is to try several and evaluate on a validation set. It's also important to remember you can mix and match activations of various layers.

2) In theory yes, many random initializations would be the same if your data was extremely well behaved and your network ideal. But in practice initializations seek to ensure the gradient starts off reasonable and the signal can be backpropagated correctly. Likely in this case any of those initializations would perform similarly, but the best approach is to try them out, switching if you get undesirable results.