Hanak questioning the approach of analyzing a single composition at a time led to the foundation of the modern-day HTE. With time, this beneficial combinatorial approach to materials development and evaluation was adopted by many disciplines, such as heterogeneous catalysis. HTE research accompanies three key challenges to be addressed, viz., experimental design, screening strategy, and data analysis. These challenges can be aptly addressed by the combination of existing knowledge and machine learning (ML) tools to narrow the parametric space as well as to avoid combinatorial explosion. In general, ML has made tremendous impacts on the analyses of complex and bulky catalysis data including those generated by HTE. It has helped researchers make catalyst discoveries through the building of predictive models for the target properties, and understand trends hidden in the data. Moreover, the effective use of tools such as the genetic algorithm (GA) can assist in the exploration of a parametric space of catalysts, hereafter called catalyst space, to improve the probability of obtaining the best-performing catalysts. As the name suggests, the GA is a search heuristic tool developed based on inspiration from Darwin's theory of evolution. This is typically based on modification, selection, and survival of the fittest, leading to better adaption to a preset target. Previous reports from our group employed a random sampling strategy for exploring quaternary catalysts for oxidative coupling of methane (OCM), followed by guiding subsequent catalyst design via MLs. However, this strategy is not easily applicable for highly multi-dimensional catalyst design, such as 14 elemental catalysts studied in this article, where hitting high-performing catalysts by random sampling can be likened to searching a needle in the haystack. GA, on the other hand, facilitates the search for a highly multidimensional catalysis space by promoting the fittest catalysts. Some of the advantages of GA as compared to other optimization tools are 1) GA is applicable for exploring a highly multidimensional space without requiring prior knowledge and 2) optimization is done in parallel, fitting well within the HTE scheme. A major drawback of using any optimization tools in exploration is its data bias, leading to distortion in the analytical results. The conclusions obtained from GA are highly dependent on the input given and the conclusions obtained might not be universal but characteristic of that particular local maximum. However, this would not be a big issue, given that the goal of exploring a huge catalyst space is to discover high-performing catalysts.