Artificial intelligence in peer review: How can evolutionary computation support journal editors?

TL;DR

This paper demonstrates that Cartesian Genetic Programming, an evolutionary algorithm, can significantly optimize peer review editorial strategies, reducing review times by 30% without additional reviewer resources.

Contribution

It introduces the application of Cartesian Genetic Programming to improve peer review processes, a novel use of evolutionary algorithms in social system optimization.

Findings

Peer review duration reduced by 30%

Evolutionary algorithms can optimize complex social systems

No increase in reviewer pool needed

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.