Self-adaptive random walk with pesudo-gradients for genetic evolution of an artificial neural network
To optimize the weights in an artificial neural network most methods rely gradients, which are not always obtainable or desirable. Evolutionary algorithms are instead based on Darwinian evolution where no derivative is needed. These algorithms have a set of strategy parameters that can be dynamically updated during the search to increase performance. Two ways of updating the parameters are the so