This produces a population consisting of newer and older generations. Another method for selecting which members of the parent generation should reproduce is called elitism. The elitism function creates a replica of one of the best chromosomes and adds it to the new generation. Elitism ensures that at least one of the best solutions generated survives until the end of the evolutionary process. To fill in the rest of a population that is using elitism to preserve the best results, processes of fitness, rank and steady state selection are used.
Elitism is considered as a more realistic function enhancing performance and results.
For example, if an encoding was based on a binary bit string of length n, then the solution space would be all 2n possible bit strings. The essential difficulty when working with a problem that can best be solved by a genetic algorithm is that the search space can be so large while a process for finding the single best solution can be so uncertain, or so difficult, that a best-guess solution becomes preferable to utilizing millions of hours of computer time solving the problem in some other way. By utilizing randomness to create new possibilities that are similar to older possibilities, genetic algorithms are able to more quickly come up with a best-guess solution. Genetic algorithms can be used to learn from experiences. Creating such programs allow researchers to follow patterns and estimate future scenarios.