the algorithm convergence (see Algorithm \[alg:GAU\] for details).\

Generation of $L$ initial solutions by populating the chromosomes through the random draw from the pool of possible gene values {0, 1}. Evaluation of the objective (fitness) function for each solution. Ranking of solutions from ’best’ to ’worst’ according to the objective function evaluation results. Selection of $K$ best solutions from the previous generation and production of $L$ new solutions by randomly changing the values of one or more genes. With $L = mK$ every one of the ’best’ solutions will be used to produce $m$ new solutions. Evaluation of the objective (fitness) function for each solution. Ranking of solutions from ’best’ to ’worst’ according to the objective function evaluation results.

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The best values of parameters $L$ and $K$ depend on the problem size and specific QUBO coefficient mapping scheme and can be found through the trial and error process. The objective here is to achieve the target convergence with the smallest number of objective function calls. The computational time of a single objective function call on the test machine (Intel(R) Xenon(R) CPU E5-1620 v4 processor run at 20% CPU utilization) has been measured to be proportional to $N^2$ up to 