I am trying to understand the genetic algorithm in terms of feature selection and these features are extracted using a machine learning algorithm.
Let's suppose I have data of heart rate for 3 minutes collected from $50$ subjects. From these 3-minute heart rate, I extracted $5$ features, like the mean, standard deviation, variance, skewness and kurtosis. Now the shape of my feature set is
I want to know what are gene, chromosome and population in genetic algorithm related to the above scenario.
What I understand is each feature is a gene, and a set of all features for one subject
(1, 5) is the chromosome, and the whole feature set
(50, 5) is a population. But I think this concept is not correct. Because in the genetic algorithm, we take a random population, but according to my concept complete data is population, so how random data is selected.
Can anyone help me to understand it?