Terminology: Difference between decision variables, features and attributes? Could there be a difference between the words "feature", "attribute", and "decision variable" when used in the same paper? The one I am specifically thinking about is about an optimization method for clustering, but I am also wondering if there generally are any scenarios for which it could be. 

I can't manage to google up an answer that either confirms or denies that these are the same thing, and I have no formal training in data science. 

EDIT: 

I left the article out at first to not omit a more general answer, but this is the link: ncbi.nlm.nih.gov/pmc/articles/PMC4532808

Some quotes: 


"Mutation is a probabilistic operator that randomly modifies a decision variable of a candidate solution." 
"Suppose [...] POPi(s) is the sth decision variable of POPi  (i.e.  C r t, t = 1,2,…, d  and  r = 1,2,…, q)." 
"[...] max⁡(h i(SIV)),  min⁡(h i(SIV)) are the upper and lower bounds for each decision variable [...]" 
"A SIV is a feature of the solution and can be imagined like a gene
in GA."
Feature and attribute have both the same meaning. 

Decision variable is the variable which is used to make a split in a decision tree in tree based algorithms, i.e. the variable on which the decision is made.