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Individuals in the population are selected to undergo crossover and mutation operations for reproduction using the roulette wheel selection [34].
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Then perform the selection, crossover and mutation operation for individuals.
The k-point crossover and uniform mutation operations were utilized for the present study.
The mutation operation of DE applies vector differentials between existing population members for determining both the degree and the direction applied to the individual subject of the mutation operation.
Monte Carlo (MC) type local optimization is used between genetic algorithm (GA) steps together with a special mutation operation designed for the cluster geometry optimization problem.
These members are used for crossover and mutation operations to create new population.
Although it is meaningful to align DNA coding regions for homologous sequences using mutation operators, alignment of binding site sequences for the same transcription factor cannot rely on evolutionary related mutation operations.
Step6: implement the crossing and mutation operations.
Hence, cross over and mutation operations are involved.
Step 5: Implementation of selection, crossover, and mutation operations.
An evolutionary optimization algorithm dealing with a population of cycles evolving by the crossing, and the mutation operations is developed.
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