Abstract: Feature ranking can have a severe impact on the feature selection problem. Feature rankingmethods refer to the structure of features that can accept the designed data and have a positive effect onthe quality of features. Moreover, accessing useful features helps in reducing cost and improving theperformance of a feature ranking algorithm. There are numerous methods for ranking the features thatare available in the literature. The developments of the past 20 years in the domain of knowledge researchhave been explored and presented in terms of relevance and various known concepts of featureranking problems. The latest developments are mostly based on the evolutionary approaches whichbroadly include variations in ranking, mutual information, entropy, mutation, parent selection, geneticalgorithm, etc. For a variety of algorithms based on differential evolution, it has been observed thatalthough the suitability of the mutation operator is extremely important for feature selection yet otheroperators can also be considered. Therefore, the special emphasis of various algorithms is observing andreviewing the algorithms and finding new research directions: The general approach is to review a rigorouscollection of articles first and then obtain the most accurate and relevant data followed by thenarrow down of research questions. Research is based on the research questions. These are reviewed infour phases: designing the review, conducting the review, analyzing, and then writing the review.Threats to validity is also considered with research questions. In this paper, many feature ranking methodshave been discussed to find further direction in feature ranking and differential evolution. A literaturesurvey is performed on 93 papers to find out the performance in relevance, redundancy, correlationwith differential evolution. Discussion is suitable for cascading the direction of differential evolution inintegration with information-theoretic, entropy, and sparse learning. As differential evolution is multiobjectivein nature so it can be incorporated with feature ranking problems. The survey is being conductedon many renowned journals and is verified with their research questions. Conclusions of thesurvey prove to be essential role models for multiple directions of a research entity. In this paper, acomprehensive view on the current-day understanding of the underlying mechanisms describing theimpact of algorithms and review current and future research directions for use of evolutionary computations,mutual information, and entropy in the field of feature ranking is complemented by the list ofpromising research directions. However, there are no strict rules for the pros and cons of alternativealgorithms.