# 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?

• What is a goal of the model? Regression, classification or something else? I think your 50 subjects are nor individuals of a population, but a part of a way to define a fitness function. Individuals of a population will be binary 5-element vectors, that represent if features are important or not. – oleg.mosalov Jun 23 '20 at 13:43
• its classification, the fitness function gives accuracy – Talha Anwar Jun 24 '20 at 0:04