Title: Artificial intelligence in peer review: How can evolutionary computation support journal editors?,
Abstract: With the volume of manuscripts submitted for publication growing every year,
the deficiencies of peer review (e.g. long review times) are becoming more
apparent. Editorial strategies, sets of guidelines designed to speed up the
process and reduce editors workloads, are treated as trade secrets by
publishing houses and are not shared publicly. To improve the effectiveness of
their strategies, editors in small publishing groups are faced with undertaking
an iterative trial-and-error approach. We show that Cartesian Genetic
Programming, a nature-inspired evolutionary algorithm, can dramatically improve
editorial strategies. The artificially evolved strategy reduced the duration of
the peer review process by 30%, without increasing the pool of reviewers (in
comparison to a typical human-developed strategy). Evolutionary computation has
typically been used in technological processes or biological ecosystems. Our
results demonstrate that genetic programs can improve real-world social systems
that are usually much harder to understand and control than physical systems.