Application of Evolutionary Artificial Intelligence. An Exploratory Literature Review

Abstract

Evolutionary processes found in nature are of interest to developers and practitioners of artificial intelligence because of the ability to optimize, detect, classify, and predict complex man-made processes. Evolutionary artificial intelligence (EAI) is examined from various perspectives to evaluate the main research directions and the trend of the decade. Co-occurrence networks were used to visualize data and find key sub-themes in a dataset consisting of article titles. The literature review covers the following aspects of EAI applications: methods, detection, data, approach, and colony. The resulting co-occurrence networks show a huge increase in diversity in research methods, data and function application possibilities, and approaches. Although simulating the behaviour of colonies is not as popular as it was a decade ago, the scope of applications for known algorithms has not been diminished.

An exploratory review of the fintech influence field

“…Co-occurrence networks are used in various ways in the scientific literature: employ them to find geographic centres and scientific institutions that develop certain fintech topics and in the analysis of the most popular keywords. Maknickiene (2022) utilised these networks to represent the dynamics of genetic algorithm topics in scientific literature, whereas Brahimi and Haneya (2023) explained the mapping of scientifical literature using WOSviever. We used co-occurrence networks only to identify topic clusters.…”