  Gomez proposes a formal and systematic approach for characterizing stochastic
global optimization algorithms. Using it, Gomez formalizes algorithms with a
fixed next-population stochastic method, i.e., algorithms defined as stationary
Markov processes. These are the cases of standard versions of hill-climbing,
parallel hill-climbing, generational genetic, steady-state genetic, and
differential evolution algorithms. This paper continues such a systematic
formal approach. First, we generalize the sufficient conditions convergence
lemma from stationary to non-stationary Markov processes. Second, we develop
Markov kernels for some selection schemes. Finally, we formalize both
simulated-annealing and evolutionary-strategies using the systematic formal
approach.
