A Discussion of Machine Learning Competitions

Define some objective, then let a population of algorithms compete against one another, mixing and refining characteristics of the most successful of their predecessors, and occasionally introducing novel approaches. They are rather ubiquitous in today's research and commercial environment, but what are they good for, and what should one know about them? I'm here to ask some of these questions, and maybe produce a few answers along the way. Although it can be misleading to mix metaphors in a discipline that already overburdens its terminology over different granularities and levels of description, I invoked the essence of genetic algorithms in order to convince people that an observable of any recurring competition is that the metric it judges contestants by tends to be optimized over time. I also wanted to get the idea of diversity and its relationship with an incentivized selection process such as competition primed in our minds.