Figure 1.

General overview of how an individual network is processed. Each individual receives an environmental signal. This signal is propagated through the signal processing section of the network and produces an output signal. The network output is used to assess the fitness of the individual. An adjacency matrix expressed the information describing a network. Each row corresponds to a node and each column represents the corresponding connection of that node. The first network describes logic representation. The second network assumed the duplication of node “3” which results in augmenting the logic driving the activity of node “2”. The same node's logic is mutated in the third network. Each index on the adjacency matrix can have a value of 0, which represents there is no edge present or a positive or negative integer. The value represents a logical function associated with the connection: All incoming edges with a functional value of 1 are combined using an OR function, while all incoming edges with a functional value of 2 are combined using an AND function. Complex functions are then defined by combining multiple functional groups. In this example the column labelled “2” represents the inputs to Node 2. The presence of 2 inputs each with a value of “2” represents that these inputs will be processed by an AND function.