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.