Genetic Algorithms: Selection

Selection is a genetic operator that chooses a chromosome from the current generation’s population for inclusion in the next generation’s population. Before making it into the next generation’s population, selected chromosomes may undergo crossover and / or mutation (depending upon the probability of crossover and mutation) in which case the offspring chromosome(s) are actually the ones that make it into the next generation’s population.

Genetic Server and Genetic Library include the following types of selection:

Roulette - A selection operator in which the chance of a chromosome getting selected is proportional to its fitness (or rank). This is where the concept of survival of the fittest comes into play.

Tournament - A selection operator which uses roulette selection N times to produce a tournament subset of chromosomes. The best chromosome in this subset is then chosen as the selected chromosome. This method of selection applies addition selective pressure over plain roulette selection.

Top Percent - A selection operator which randomly selects a chromosome from the top N percent of the population as specified by the user.

Best - A selection operator which selects the best chromosome (as determined by fitness). If there are two or more chromosomes with the same best fitness, one of them is chosen randomly.

Random - A selection operator which randomly selects a chromosome from the population.