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The probability of crossover and mutation are adjusted dynamically according to the generation number and the fitness value.
The probability of crossover and mutation does not have to be constant as in classical genetic algorithms and it can change during the evolutionary process.
A sensitivity analysis was performed for major parameters such as the numbers of generations, population sizes and probability of crossover and mutation, in order to determine a good fitness level and convergence for optimum solutions.
Probability of crossover = 0.6.
CR is the probability of crossover.
Probability of crossover ((P_text {c})) = 0.85.
Similar(19)
Based on these, the adaptive probabilities of crossover and mutation operators of an individual are presented.
Based on these, the adaptive probabilities of crossover and mutation operation of an evolutionary individual are proposed.
The proposed optimization algorithm considers the adaptive probabilities of crossover and mutation which change with the fitness values of individuals and proposes a penalty function to deal with the burst pressure constraint.
Parameters include population size, number of generations, and probabilities of crossover and mutation.
The probabilities of crossover and mutation for each generation are adaptively determined, which can overcome the premature convergence and the slow convergence speed in later evolutionary processes.
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