Figure 2

Likelihood of finding a viable individual with a random input vector in the discrete and continuous models. The Discrete Output Vector model (DOV) is an adaptation of Wagner's original model with continuous values in the network and discrete values in the output vector, resulting from a choice of a = 100 in Equation 3. The additional requirements for stability still hold (minimum population variance of 0.1). The continuous output vector model (COV) rarely yields viable individuals in networks with a small number of genes, but quickly matches and exceeds the likelihood of the DOV in networks with 6 genes. With more than 7 genes, the COV is actually more efficient at yielding viable individuals than the DOV, while maintaining a higher population variance (not shown).