In natural populations, and in genetic algorithms, these decisions are automatic; whereas in typical HBGA systems, they are made by people.
In most organic and computer-based systems (top and bottom), innovation is automatic, operating without human intervention.
The main difference lies in the genetic material they work with: electronic data vs. polynucleotide sequences.
The HBGA methodology was derived in 1999-2000 from analysis of the Free Knowledge Exchange project that was launched in the summer of 1998, in Russia (Kosorukoff, 1999).
Recent research suggests that human-based innovation operators are advantageous not only where it is hard to design an efficient computational mutation and/or crossover (e.g. when evolving solutions in natural language), but also in the case where good computational innovation operators are readily available, e.g. when evolving an abstract picture or colors (Cheng and Kosorukoff, 2004).