  Though biological and artificial complex systems having inhibitory
connections exhibit high degree of clustering in their interaction pattern, the
evolutionary origin of clustering in such systems remains a challenging
problem. Using genetic algorithm we demonstrate that inhibition is required in
the evolution of clique structure from primary random architecture, in which
the fitness function is assigned based on the largest eigenvalue. Further, the
distribution of triads over nodes of the network evolved from mixed connections
exhibits a negative correlation with its degree providing insight into origin
of this trend observed in real networks.
