What is the advantage of using evolutionary algos in this case over using something like Thompson Sampling or Contextual Multi-Armed Bandit? (http://www.kdd.org/kdd2017/papers/view/an-efficient-bandit-a...)
Thompson Sampling / Multi-armed bandit algorithms are traffic allocation strategies to use the least amount of traffic to find the optimal variant.
So say you have version A/B/../N variants of a website, you can split the traffic equally or use a a bandit algorithm and find the best performing variant of it.
But that only helps you test N variants. If you want to test for 4 titles and 3 color buttons, 3 layouts and 5 images you already have 4x3x3x5=180 different variations and you can't test them all. Evolutionary algorithms can help you search through a much bigger search space:
First you try 10 variations, and allocate traffic equally or through a bandit algorithm. Then you combine the top performing candidates multiple times to create a new generation and start over again.
Evolution helps you find which candidates to try in a large search space and then a bandits algorithm can help you allocate traffic optimally.
You have to be careful with A/B testing with Bandit algorithms though. If the conversion rate changes over time or if you have visitors that don't always convert instantly you have to take that into account: https://www.chrisstucchio.com/blog/2015/dont_use_bandits.htm...
I don't think they account for potentially changing conversion rates over time or delayed conversions.
Aside from that, I'd be curious to see how these two approaches compare in a real-life situation.
The allocation of traffic based on the evolving optimal search space (blue button for visitors from Facebook) can then be driven through an MAB or something similar.