  We show how the characteristics of the evolutionary algorithm influence the
evolvability of candidate solutions, i.e. the propensity of evolving
individuals to generate better solutions as a result of genetic variation. More
specifically, (1+{\lambda}) evolutionary strategies largely outperform
({\mu}+1) evolutionary strategies in the context of the evolution of digital
circuits --- a domain characterized by a high level of neutrality. This
difference is due to the fact that the competition for robustness to mutations
among the circuits evolved with ({\mu}+1) evolutionary strategies leads to the
selection of phenotypically simple but low evolvable circuits. These circuits
achieve robustness by minimizing the number of functional genes rather than by
relying on redundancy or degeneracy to buffer the effects of mutations. The
analysis of these factors enabled us to design a new evolutionary algorithm,
named Parallel Stochastic Hill Climber (PSHC), which outperforms the other two
methods considered.
