Artificial intelligence in peer review: How can evolutionary computation support journal editors?
This addresses the workload and time delays faced by journal editors in small publishing groups, offering a novel application of evolutionary computation to social systems.
The paper tackled the problem of inefficient peer review processes in academic publishing by using Cartesian Genetic Programming to evolve editorial strategies, resulting in a 30% reduction in review duration without requiring additional reviewers.
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.