On the use of biologically-inspired adaptive mutations to evolve artificial neural network structures

Evolutionary algorithms have been used to successfully evolve artificial neural network structures. Normally the evolutionary algorithm has several different mutation operators available to randomly change the number and location of neurons or connections. The scope of any mutation is typically limited by a user-selected parameter. Nature, however, controls the total number of neurons and synaptic connections in more predictable ways, which suggests the methods typically used by evolutionary algorithms may be inefficient. This paper describes a simple evolutionary algorithm that adaptively mutates the network structure where the adaptation emulates neuron and synaptic growth in the rhesus monkey. Our preliminary results indicate it is possible to evolve relatively sparse connected networks that exhibit quite reasonable performance.