A Comparative Study of EAG and PBIL on Large-Scale Global Optimization Problems

Pseudocode 1

The PBIL algorithm.

Require:

(learning rate), (mutation rate), (mutation shift),

(length of the probability vector), and (population size)

(1) Initialize the probability vector

(2) Repeat Steps  3 to 7 until stopping criteria are met

(3) Generate a population of solutions using

(4) Evaluate the fitness of the solutions (generated in Step  3)

(5) Select the best solutions from

(6) Update the probability vector as following:

(7) Mutate the probability vector as following:

   if random then