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The genetic operation usually consists of inheritance, mutation, and crossover.
For mutation and crossover random elite candidates are chosen.
They usually contain both mutation and crossover operators.
The variation in DE contains both mutation and crossover operators.
Mutation and crossover operations are necessary operators in these algorithms.
Mutation and crossover techniques in the GA avoid trapping in the local optimum.
For example, GA contains parameters, such as mutation and crossover probabilities.
The probabilities of mutation and crossover were 0.15 and 0.8, respectively.
Czarn et al. [12] studied the influence of two genetic algorithm parameters: mutation and crossover rates.
In common genetic algorithms the mutation and crossover techniques are used to optimize an objective function.
Besides these newly proposed operators, mutation and crossover operators have been used.
More suggestions(16)
mutation and screening
mutation and speciation
mutation and survival
mutation and elitism
mutation and evolution
mutation and substitution
mutation and cross-over
mutation and selection
mutation and inversion
mutation and recombination
mutation and phenotype
mutation and tumour
mutation and translation
mutation and replacement
mutation and polymorphism
mutation and chromosome
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