How to apply AdaBoost to more "complex" (non-binary) classifications/data fitting?
AdaBoost is a supervised learning method; it starts with a table of 'correct' answers and generates a predictive model for a target, which is known. It is then possible to inspect this model to figure out how it works, what it judged was more important. With that in mind, here is my guess for what he did:

First, he created a training dataset. The dataset was created, according to the article, like this: 


  Now he’d do the same for love. First he’d need data. While his
  dissertation work continued to run on the side, he set up 12 fake
  OkCupid accounts and wrote a Python script to manage them. The script
  would search his target demographic (heterosexual and bisexual women
  between the ages of 25 and 45), visit their pages, and scrape their
  profiles for every scrap of available information: ethnicity, height,
  smoker or nonsmoker, astrological sign—“all that crap,” he says.
  
  To find the survey answers, he had to do a bit of extra sleuthing.
  OkCupid lets users see the responses of others, but only to questions
  they’ve answered themselves. McKinlay set up his bots to simply answer
  each question randomly—he wasn’t using the dummy profiles to attract
  any of the women, so the answers didn’t mat­ter—then scooped the
  women’s answers into a database. "


So he used python scripts to collect lots of information! At the end of this, he had a table of data, where each row had three pieces of information:
- A bot's answer to all the questions, and their weights
- A woman's answer to all the questions
- Their match percentage. 

On this, he could use AdaBoost to create a predictive model, predicting the match percentage from the available information. The weak learners were probably decision stumps, greedily choosing one variable at a time to split on, that's the standard that people refer to when talking about AdaBoost. 

Once the predictive model, was in place, it could be used for optimization - determining which weights to put on the questions when holding all other variables constant, maximizing the average match percentage to women in his target audience. 

Of course, this is just a guess. The article doesn't have much detail. But it's a potential way to use AdaBoost for that purpose.