Self-adaptive random walk with pesudo-gradients for genetic evolution of an artificial neural network

Sammanfattning: 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 called ``1/5th-rule", which uses the offspring survival rate to self adapt, and random mutation which uses inheritance and mutation to evolve the strategy parameters as well. We present an algorithm that combines the aspects of these two self adaptation methods by changing strategy parameters differently for new offspring and older survivors. We also introduce a pseudo-gradient by adding a memory of the previous step taken in the search space and let the new mutation be shifted by this remembered step. In this investigation these two methods failed to improve the performance over the ``1/5th-rule" but performed better than the random mutation. The new algorithms showed promising results regarding combining the aspects of the ``1/5th-rule" and random mutation.