Editing Genetic operator (section)

===Crossover===
{{main|Crossover (evolutionary algorithm)}}
Crossover is the process of taking more than one parent solutions (chromosomes) and producing a child solution from them. By recombining portions of good solutions, the evolutionary algorithm is more likely to create a better solution.<ref name=ga-intro /> As with selection, there are a number of different methods for combining the parent solutions, including the ''edge recombination operator'' (ERO) and the 'cut and splice crossover' and 'uniform crossover' methods. The crossover method is often chosen to closely match the chromosome's representation of the solution; this may become particularly important when variables are grouped together as [[Genetic algorithm#The building block hypothesis|building blocks]], which might be disrupted by a non-respectful crossover operator. Similarly, crossover methods may be particularly suited to certain problems; the ERO is considered a good option for solving the [[travelling salesman problem]].<ref>{{Citation |last1=Whitley |first1=Darrell |last2=Starkweather |first2=Timothy |last3=Fuquay |first3=D'Ann |title=Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator |date=1989 |work=Proceedings of the 3rd International Conference on Genetic Algorithms (ICGA) |pages=133–140 |editor-last=Schaffer |editor-first=J.D. |place=San Francisco |publisher=Morgan Kaufmann |isbn=1558600663 }}</ref>