Currently I was studying the genetic algorithm and I got it very interesting area to work in but as per my new field I am not getting any idea about the topic. So I want to know that "What are the current problems yet to be solved in Genetic Algorithms?". Thanks!
PS: I read the concepts and still working to improve knowledge in it still ideas and problems are most welcomed.
If I understand your question correctly, I personally think you should rephrase your question to "What are the current problems yet to be solved in Genetic Algorithms?".
In regards to your answer, I'm also studying Genetic Algorithms (GA) and I can't say I'm an an expert, however there some of the things that plagues GAs:
finding the absolute optimal when searching through an in-complete search space. If you need to guarantee absolute best, it is best to user other methods.
Then there is the encoding problem, there are many problems where you can't really encode your problem into a GA.
Defining a sound evaluation function, how you define the best.
Converging in a dynamic environment, GA assumes the environment or search landscape is static
Perform poorly with Boolean evaluation functions (Yes/No, On/Off functions)
For more information of what problems are hard for GAs is this paper, "What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation"
As for applications of GA, the list is huge, see here