What are the advantages/disadvantages of design of experiments (DoE) versus stochastic optimization methods I am working in a project to assist an experimental team in optimizing reaction conditions. The problem involves a large number of dimensions, i.e. 30+ reactants which we are trying out different concentrations to achieve the highest yield of a certain product.
I am familiar with stochastic optimization methods such as simulated annealing, genetic algorithms, which seemed like a good approach to this problem. The experimental team proposes using design of experiments (DoE), which I'm not too familiar with.
So my question is, what are the advantages/disadvantages of DoE (namely fractional factorial and response surface method I believe) versus stochastic optimization methods, and are there use cases where one is preferred over the other?

Stochastic optimization (SO) and the design of experiments (DOE) are entirely differ from each other, in terms of why using it, they are not orthogonal and simply you might use both of them at the same time. You might consider DOE as a measurement of how much  changes in genes (variation) affect the production, (ex. we might have n-production comes from SO), which implies that DOE is convenient for optimizing multiple-productions, but SO aims to optimize an initial production X.
