Editing Genetic algorithm (section)

==== Selection ====
{{Main|Selection (genetic algorithm)}}
During each successive generation, a portion of the existing population is [[selection (genetic algorithm)|selected]] to reproduce for a new generation. Individual solutions are selected through a ''fitness-based'' process, where [[Fitness (biology)|fitter]] solutions (as measured by a [[fitness function]]) are typically more likely to be selected. Certain selection methods rate the fitness of each solution and preferentially select the best solutions. Other methods rate only a random sample of the population, as the former process may be very time-consuming.

The fitness function is defined over the genetic representation and measures the ''quality'' of the represented solution. The fitness function is always problem-dependent. For instance, in the [[knapsack problem]] one wants to maximize the total value of objects that can be put in a knapsack of some fixed capacity. A representation of a solution might be an array of bits, where each bit represents a different object, and the value of the bit (0 or 1) represents whether or not the object is in the knapsack. Not every such representation is valid, as the size of objects may exceed the capacity of the knapsack. The ''fitness'' of the solution is the sum of values of all objects in the knapsack if the representation is valid, or 0 otherwise.

In some problems, it is hard or even impossible to define the fitness expression; in these cases, a [[Computer simulation|simulation]] may be used to determine the fitness function value of a [[phenotype]] (e.g. [[computational fluid dynamics]] is used to determine the air resistance of a vehicle whose shape is encoded as the phenotype), or even [[Interactive evolutionary computation|interactive genetic algorithms]] are used.