This discussion explores the effectiveness of simulated annealing compared to random search for optimizing a set of 16 integer parameters. The author seeks to determine if simulated annealing provides a significant advantage over random search, despite the parameter space being too large for exhaustive search. Responses suggest plotting performance over time and highlight the ability of simulated annealing to escape local optima as its main strength.
A/B testing is meant for strict experiments where focus is on statistical significance, whereas multi-armed bandit algorithms are meant for continuous optimization where focus is on maintaining higher average conversion rate.
