Selection is the stage of a genetic algorithm or more general evolutionary algorithm in which individual genomes are chosen from a population for later breeding (e.g., using the crossover operator). A selection procedure used early on may be implemented as follows: The fitness values that have been computed (fitness function) are normalized, such that the sum of all resulting fitness values equals 1. Accumulated normalized fitness values are computed: the accumulated fitness value of an individual is the sum of its own fitness value plus the fitness values of all the previous individuals; the accumulated fitness of the last individual should be 1, otherwise something went wrong in the normalization step. A random number R between 0 and 1 is chosen. The selected individual is the first one whose accumulated normalized value is greater than or equal to R. For many problems the above algorithm might be computationally demanding. A simpler and faster alternative uses the so-called stochastic acceptance. If this procedure is repeated until there are enough selected individuals, this selection method is called fitness proportionate selection or roulette-wheel selection. If instead of a single pointer spun multiple times, there are multiple, equally spaced pointers on a wheel that is spun once, it is called stochastic universal sampling. Repeatedly selecting the best individual of a randomly chosen subset is tournament selection. Taking the best half, third or another proportion of the individuals is truncation selection. There are other selection algorithms that do not consider all individuals for selection, but only those with a fitness value that is higher than a given (arbitrary) constant. Other algorithms select from a restricted pool where only a certain percentage of the individuals are allowed, based on fitness value. Selection mechanisms are also used to choose candidate solutions (individuals) for the next generation. Retaining the best individuals in a generation unchanged in the next generation, is called elitism or elitist selection. It is a successful (slight) variant of the general process of constructing a new population. Methods of Selection (Evolutionary Algorithm) The listed methods differ mainly in the selection pressure, which can be set by a strategy parameter in the rank selection described below. The higher the selection pressure, the faster a population converges against a certain solution and the search space may not be explored sufficiently. For more selection methods and further detail see. Roulette Wheel Selection In the roulette wheel selection, the