Editing Genetic operator (section)

==Operators==
Genetic variation is a necessity for the process of [[evolution]]. Genetic operators used in evolutionary algorithms are analogous to those in the natural world: [[survival of the fittest]], or [[selection (genetic algorithm)|selection]]; reproduction ([[crossover (genetic algorithm)|crossover]], also called recombination); and [[mutation (genetic algorithm)|mutation]].

===Selection===
{{main|Selection (evolutionary algorithm)}}
Selection operators give preference to better candidate solutions (chromosomes), allowing them to pass on their 'genes' to the next generation ([[iteration]]) of the algorithm. The best solutions are determined using some form of [[objective function]] (also known as a '[[fitness function]]' in evolutionary algorithms), before being passed to the crossover operator. Different methods for choosing the best solutions exist, for example, [[fitness proportionate selection]] and [[tournament selection]].<ref>{{Cite book |last1=Eiben |first1=A.E. |title=Introduction to Evolutionary Computing |last2=Smith |first2=J.E. |date=2015 |publisher=Springer |isbn=978-3-662-44873-1 |edition=2nd |series=Natural Computing Series |location=Berlin, Heidelberg |pages=80–87 |language=en |chapter=Parent Selection |doi=10.1007/978-3-662-44874-8}}</ref> A further or the same selection operator is used to determine the individuals for being selected to form the next parental generation. The selection operator may also ensure that the best solution(s) from the current generation always become(s) a member of the next generation without being altered;<ref>{{Cite book |last1=Eiben |first1=A.E. |url=https://link.springer.com/10.1007/978-3-662-44874-8 |title=Introduction to Evolutionary Computing |last2=Smith |first2=J.E. |date=2015 |publisher=Springer |isbn=978-3-662-44873-1 |edition=2nd |series=Natural Computing Series |location=Berlin, Heidelberg |pages=87–90 |language=en |chapter=Survivor Selection |doi=10.1007/978-3-662-44874-8}}</ref> this is known as ''elitism'' or ''elitist selection''.<ref name=ga-intro /><ref name=ga-intro2>{{cite web|title=Introduction to Genetic Algorithm|url=http://www.rennard.org/alife/english/gavintrgb.html|accessdate=20 August 2015}}</ref><ref>{{Cite book |last1=Eiben |first1=A.E. |title=Introduction to Evolutionary Computing |last2=Smith |first2=J.E. |date=2015 |publisher=Springer |isbn=978-3-662-44873-1 |edition=2nd |series=Natural Computing Series |location=Berlin, Heidelberg |pages=89 |language=en |doi=10.1007/978-3-662-44874-8}}</ref>

===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>

===Mutation===
{{main|Mutation (evolutionary algorithm)}}
The mutation operator encourages genetic diversity amongst solutions and attempts to prevent the evolutionary algorithm converging to a [[local minimum]] by stopping the solutions becoming too close to one another. In mutating the current pool of solutions, a given solution may change between slightly and entirely from the previous solution.<ref name=":3">{{Cite book |last1=Bäck |first1=Thomas |url=https://www.worldcat.org/oclc/45730387 |title=Evolutionary computation Vol. 1, Basic algorithms and operators |last2=Fogel |first2=David B. |last3=Whitley |first3=Darrell |last4=Angeline |first4=Peter J. |publisher=CRC Press |year=1999 |isbn=0-585-30560-9 |editor-last=Bäck |editor-first=Thomas |location=Boca Racon |pages=237–255 |language=en |chapter=Mutation operators |oclc=45730387 |editor-last2=Fogel |editor-first2=David B. |editor-last3=Michalewicz |editor-first3=Zbigniew}}</ref> By mutating the solutions, an evolutionary algorithm can reach an improved solution solely through the mutation operator.<ref name=ga-intro /> Again, different methods of mutation may be used; these range from a simple ''bit mutation'' (flipping random bits in a binary string chromosome with some low probability) to more complex mutation methods in which genes in the solution are changed, for example by adding a random value from the [[Gaussian distribution]] to the current gene value. As with the crossover operator, the mutation method is usually chosen to match the representation of the solution within the chromosome.<ref name=":3" /><ref name=":0">{{Cite book |last1=Eiben |first1=A.E. |url=http://link.springer.com/10.1007/978-3-662-44874-8 |title=Introduction to Evolutionary Computing |last2=Smith |first2=J.E. |date=2015 |publisher=Springer |isbn=978-3-662-44873-1 |series=Natural Computing Series |location=Berlin, Heidelberg |pages=49–78 |chapter=Representation, Mutation, and Recombination |doi=10.1007/978-3-662-44874-8|s2cid=20912932 }}</ref>