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

Maciej J Mrowinski¹, Piotr Fronczak¹, Agata Fronczak¹

¹Faculty of Physics, Warsaw University of Technology, Koszykowa 75, PL-00-662, Warsaw, Poland.

|September 21, 2017

Summary

This study demonstrates that evolutionary algorithms can significantly enhance editorial strategies for academic publishing. Artificial intelligence improved peer review efficiency by 30%, reducing publication times.

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Area of Science:

Computational intelligence
Bibliometrics
Scientific publishing

Background:

The increasing volume of manuscript submissions highlights inefficiencies in traditional peer review processes, such as extended review durations.
Current editorial strategies are often proprietary, hindering broader adoption and improvement.
Small publishing groups typically rely on iterative trial-and-error methods to refine their editorial workflows.

Purpose of the Study:

To investigate the application of evolutionary computation for optimizing editorial strategies in academic publishing.
To determine if artificial intelligence can reduce peer review times and editor workload without expanding reviewer pools.

Main Methods:

Cartesian Genetic Programming (CGP), a nature-inspired evolutionary algorithm, was employed to evolve novel editorial strategies.
The performance of the evolved strategies was compared against human-developed strategies in terms of peer review duration.

Main Results:

The CGP-evolved editorial strategy reduced the peer review process duration by 30% compared to human-developed strategies.
This improvement was achieved without necessitating an increase in the number of available reviewers.
The study successfully applied evolutionary computation to a complex social system, demonstrating its potential beyond technological or biological domains.

Conclusions:

Evolutionary computation, specifically Cartesian Genetic Programming, offers a powerful method for optimizing editorial strategies in academic publishing.
AI-driven approaches can significantly enhance the efficiency of the peer review process, addressing critical bottlenecks in scholarly communication.
The findings suggest a broader applicability of evolutionary algorithms to improve complex real-world social systems.