Genetic Weight Optimization of a Feedforward Neural Network Controller

The optimization of the weights of a feedforward neural network with a genetic algorithm is discussed. The search by the recombination operator is hampered by the existence of two functional equivalent symmetries in feedforward neural networks. To sidestep these representation redundancies we reorder the hidden neurons on the genotype before recombination according to a weight sign matching criterion, and flip the weight signs of a hidden neuron’s connections whenever there are more inhibitory than excitatory incoming and outgoing links. As an example we optimize a feedforward neural network that implements a nonlinear optimal control law. The neural controller has to swing up the inverted pendulum from its lower equilibrium point to its upper equilibrium point and stabilize it there. Finding the weights of the network represents a nonlinear optimization problem which is solved by the genetic algorithm.