Reproduction, Crossover, and Mutation of Genetic Algorithm Operations Essay

Reproduction, Crossover, and Mutation of Genetic Algorithm Operations Essay

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Genetic Algorithm Operations
The basic GA that can produce acceptable results in many practical problems is composed of three operations:
a. Reproduction
b. Crossover
c. Mutation
The reproduction process is to allow the genetic information, stored in the good fitness for survive the next generation of the artificial strings, whereas the population's string has assigned a value and its aptitude in the object function. This value has the probability of being chosen as the parent in the reproduction process of a new generation.

The crossover is a process is divided into segments, which are exchanged with the one segments with the another string. With these process two new strings different to those that produced they are generated. It is necessary to clarify that the choice of strings crossed inside those that were chosen previously in the reproduction process is random. From the point of view of problem optimization, it is equal to the exploitation of an area of the parameters space.

The mutation is manifested with a small change in the genetic string of the individuals. In the case of artificial genetic strings, the mutation is equal to a change in the elementary portion (allele) of the individuals’ code. The mutation takes place with characteristics different to those that the individuals had at the beginning, characteristics that didn't possibly exist in the population. From the point of view of problem optimization, it is equal to a change of the search area in the parameters space.

Genetic algorithm basic parameters
The convergence of the GA to a suitable solution depends on its basic parameter like reproduction, crossover, mutation, selection and population; which to find a relationship among them to maintain search robust...


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...taken objective, which takes into focus the economic aspects (deposition efficiency) and the geometric characteristics (penetration, width and reinforcement) of the bead.
It was seen that the Genetic Algorithm can be a powerful tool in welding experiment optimization, even when the experimenter does not have a model for the process. The most important response (depth of penetration) had a difference from its target lower than 4%.
However, the optimization carried by GA technology requires a better setting of its own parameters, such as number of generations, population size, etc. Otherwise, there may be a risk of an insufficient sweeping of the search space system. In addition, it is suggested the use of conventional projects to research the space around the conditions found by the GA, in order to obtain models and/or perform a fine-tuning of the optimal parameters.

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