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Important part of Process - canonical genetic algorithm:
In such a scenario evolution or random mutations are often highly deleterious or can say harmful to the organism it means the change in the DNA leads to big changes in the body works. Further it may seem sensible to protect the children of the fittest individuals in the population from the mutation procedure using special alterations to the flipping probability distribution. Conversely it may be that it is actually the fittest individuals that are causing the population to stay in the local maxima. Hence after all, they get to reproduce with higher frequency. So now protecting their offspring is not a good idea, especially when, the GA will record the best from each generation, so here we won't lose their good abilities totally. Thus random mutation has been shown to be effective at getting GA searches out of local maxima effectively that is why it is an important part of the process.
Conversely to summarize the production of one generation from the previous as: now firstly, an intermediate population is create through selecting copies of the fittest individuals using probability by that every individual has at least a chance of going into the intermediate population. And now secondly, pairs from this intermediate population are chosen at random for reproduction like a pair might consist of the same individual twice, and the pair reproduce with a given fixed probability. And now thirdly in which offspring are generated through recombination procedures like as 1-point crossover, 2-point crossover and inversion. Hence finally, the offspring are randomly mutated to produce the next generation of individuals. Individuals from the old generation may be entirely killed off, means some may be allowed into the next generation alternatively there the recombination procedure might be tuned to leave some individuals unchanged. So the following schematic gives an indication that how the new generation is produced like:
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i want an assignment on application of integraton and my topc of assifment is area under the curve includin solved examples
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