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The parameters are population size, probability of mutation, probability of crossover, and selection.
The algorithm has been tested for history matching using crossover probability of 0.5 and mutation probability of 0.001 and 0.005.
Crossover probability of (p_{c} = 1) with 50%% chance of variable exchange between offspring, and mutation probability of (p_{m} = 1/5), are used.
Using a mutation operator, a few genes of a candidate chromosome are randomly selected to change their values based on a predetermined mutation probability of pm.
However, the HGA with a mutation probability of 0.01 is found to obtain the best solutions, which would be considered for later study.
In the simulation, the NSGA-II has a population size of 50, a crossover probability of 0.4, a mutation probability of 0.1, and evolves for 200 generations.
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Through the parametric study of crossover and mutation probabilities of the optimization algorithm, the results of the approach are generally satisfactory.
Step 3: Initialize the GA parameters involved in the nested optimization, including the population sizes, maximum iteration numbers, crossover and mutation probabilities of the inner and outer layer GAs.
The second class of lineage selection effects is mediated by changes of mutation probabilities of individual genes, regulatory elements, chromosomes and chromosome fragments.
Whereas these tumor-specific mutations were conserved in the patient's lymph node- and liver metastases, the heterozygous sequence variants were lost in metastases derived cell lines (H-STS and L-STS), indicating a lack of selective pressure to maintain the mutations (probability of < 0.1% for the random loss of 10/10 mutations) during in vitro-cultivation.
Table 7 Parameters for NSGA-II Symbol Value Population size 50 Max evaluations 2000 Crossover operator Single-point crossover Crossover probability 0.9 Mutation operator Bit-flip mutation Mutation probability 1/number of variables Selection operator Binary tournament2.
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