What is meant by gene, chromosome, population in genetic algorithm in terms of feature selection? 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 (50, 5).
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?
A chromosome in this case could be a set of filters, each extracting a different feature (analogous to Convolutional Neural Network). Your question doesn't say what you want to do with these features, so this solution is made under the assumption that there is a fitness function which would take these features as an input and output a score. Then, each gene is a parameter for a filter, each chromosome defines a set of such filters, which makes up an individual. A population is a set of such individuals.