A clarification on the taxonomy of Evolutionary Algorithms
I'm not 100% sure I understand your formulation (things like "our current hypothesis is neutral compared to our current hypothesis" are a bit confusing), but if I understand it correctly, it's this:

You have a current solution and at each time step, you generate a new one based on the current one and some random variation operator. If the new one is better than the current one, you accept the new one. Otherwise, you keep the current one.

That's a $(1+1)$-ES. 

A $(1,1)$-ES always accepts the new hypothesis/solution, regardless of quality. A $(1,1)$-ES is just a random walk on the graph defined by your variation operator. For a $(\mu$, $\lambda)$-ES, the algorithm generates $\lambda$ hypotheses at each step, and selects the best $\mu$ of them to form the population for the next generation -- the current hypotheses never survive (unless your variation operator produces a copy of one of them). The $(\mu+\lambda)$ strategy takes the best $\mu$ from the union of the current population and the new one.