This lecture discusses basic principles of Genetic Algorithms, a class of evolutionary computational algorithms. Genetic algorithms are based on the concept of biological evolution. As in biological science there is concept of how species are born due to union of male and female type of species and new generation take place of older generation, in the similar way due to union of candidate solutions [ chromosomes ] new candidate solutions [ chromosomes ] are being produced. New candidate solutions may mutate and may be ignored in new population if they do not meet the good-fit criteria. It is similar to Darwin’s “survival-of-fittest” theory. New chromosomes [ candidate solutions ] which are good fit or desirable [ better children ] will move forward to next level of evolution and rest will be discarded. This evolution loop continues till the desired quality solution is archived. These algorithms are mainly used for NP-Hard kind of problems and provide approximate solutions to the problem.
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