To investigate the behavior of simulated annealing on a particular problem, it can be useful to consider the ''transition probabilities'' that result from the various design choices made in the implementation of the algorithm. For each edge <math>(s,s')</math> of the search graph, the transition probability is defined as the probability that the simulated annealing algorithm will move to state <math>s'</math> when its current state is <math>s</math>. This probability depends on the current temperature as specified by {{code|temperature()}}, on the order in which the candidate moves are generated by the {{code|neighbour()}} function, and on the acceptance probability function {{code|P()}}. (Note that the transition probability is '''not''' simply <math>P(e, e', T)</math>, because the candidates are tested serially.) | To investigate the behavior of simulated annealing on a particular problem, it can be useful to consider the ''transition probabilities'' that result from the various design choices made in the implementation of the algorithm. For each edge <math>(s,s')</math> of the search graph, the transition probability is defined as the probability that the simulated annealing algorithm will move to state <math>s'</math> when its current state is <math>s</math>. This probability depends on the current temperature as specified by {{code|temperature()}}, on the order in which the candidate moves are generated by the {{code|neighbour()}} function, and on the acceptance probability function {{code|P()}}. (Note that the transition probability is '''not''' simply <math>P(e, e', T)</math>, because the candidates are tested serially.) |