Evolving neural network structures by means of genetic programming

The goal of this paper is to present a more efficient way to automatically construct appropriate neural network topologies as well as their initial weight settings. Our approach combines evolutionary algorithms and genetic programming techniques and is based on a new network encoding schema where instead of a string like encoding the graph representation of neural nets is used. This way of "encoding" reduces the computational expense and leads to a greater variety of network topologies.