1) The document discusses different algorithms for dynamically selecting between creative versions in online advertising, including weight-based, recency-weighted, and Bayesian beta bandit approaches. 2) It evaluates the performance of these algorithms in different environments like stable, noisy, and cold-start conditions using metrics like average regret and area under the cumulative regret curve. 3) The results show that Bayesian beta bandits generally perform best, but the optimal parameters depend on the environment characteristics, and auto-tuning these parameters is challenging.