Explain Working Mechanism Of Genetic Algorithms.

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  • GAs implement optimization techniques by simulating this natural law of evolution in the biological world.
  • We start with a population of randomly generated solutions. Each of these solutions is evaluated to determine how good or bad it is. In other words to determine how “fit” that solution is. We then check a terminating condition, to see if our solutions are good enough? If yes, we stop. If not, we have to optimize the solutions.
  • So, we select the best solutions from the initial population (selection). This is similar to “natural selection”.
  • Then we allow these good solutions to exchange their information, in order to get even better solutions. This step is similar to reproduction among animals or crossover among chromosomes and is called “cross-over”. We may then randomly mutate some small % of the solutions thus obtained after crossover.
  • The mutation is very important. It could be a bad thing, it could be a good thing. In the biological sense, it means, making a small change in a gene. In GAs, it means, making a small change to the solution.
  • Then again, we evaluate each of the solutions and check the termination condition. As you see, this is an optimization method.

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