Like the N-convex algorithm, this algorithm attempts to find a set of candidates whose centroid is close to . The key difference is that instead of taking unique candidates, we allow candidates to populate the set multiple times. The result is that the weight of each candidate is simply given by its frequency in the list, which we can then index by random selection:
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"He carries the load of home and family life. It still probably raises an eyebrow when he's called into a meeting and he says it has to be between 10am and 3pm. They'll be shocked that a man has said that," says Begg.