Genetic Algorithms (GAs) are members of a general class of optimization algorithms, known as Evolutionary Algorithms (EAs), which simulate a fictional environment based on theory of evolution to deal with various types of mathematical problem, especially those related to optimization. Also Genetic Algorithms can be categorized as a subset of Metaheuristics, which are general-purpose tools and algorithms to solve optimization ...

Clustering is an unsupervised machine learning task and many real world problems can be stated as and converted to this kind of problems. Clustering is grouping a set of  data objects is such a way that similarity of members of a group (or cluster) is maximized and on the other hand, similarity of members in two different groups, is minimized. ...

Genetic Algorithms (GAs) are most famous Evolutionary Algorithms (EAs) which are inspired from natural evolution and selection. Their main application is in the field of optimization. Hence they are applicable to any kind of problem, which can be converted or stated as an optimization task. To read more about Genetic Algorithms, you can see the related article in Wikipedia (here). ...