Editing Crossover (evolutionary algorithm) (section)

==Crossover for binary arrays==
Traditional genetic algorithms store genetic information in a [[chromosome (genetic algorithm)|chromosome]] represented by a [[bit array]]. Crossover methods for bit arrays are popular and an illustrative example of [[genetic recombination]].

===One-point crossover===
A point on both parents' chromosomes is picked randomly, and designated a 'crossover point'. Bits to the right of that point are swapped between the two parent chromosomes. This results in two offspring, each carrying some genetic information from both parents.

[[File:OnePointCrossover.svg]]

===Two-point and k-point crossover===
In two-point crossover, two crossover points are picked randomly from the parent chromosomes. The bits in between the two points are swapped between the parent organisms. 

[[File:TwoPointCrossover.svg|TwoPointCrossover.svg]]

Two-point crossover is equivalent to performing two single-point crossovers with different crossover points. This strategy can be generalized to k-point crossover for any positive integer k, picking k crossover points.

=== Uniform crossover===
In uniform crossover, typically, each bit is chosen from either parent with equal probability.<ref>{{Citation |last=Syswerda |first=Gilbert |title=Uniform crossover in genetic algorithms |date=1989 |work=Proceedings of the 3rd International Conference on Genetic Algorithms (ICGA) |pages=2–9 |editor-last=Schaffer |editor-first=J.D. |place=San Francisco |publisher=Morgan Kaufmann |isbn=1558600663 }}</ref> Other mixing ratios are sometimes used, resulting in offspring which inherit more genetic information from one parent than the other.
In a uniform crossover, we don’t divide the chromosome into segments, rather we treat each gene separately. In this, we essentially flip a coin for each chromosome to decide whether or not it will be included in the off-spring.