They can actually work together. Thompson Sampling / Multi-armed bandit algorithms are traffic allocation strategies to use the least amount of traffic to find

They can actually work together. 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.html https://www.chrisstucchio.com/blog/2015/dont_use_bandits.htm...