Editing Chromosome (evolutionary algorithm) (section)

==Chromosome design==
When creating the [[genetic representation]] of a task, it is determined which decision variables and other degrees of freedom of the task should be improved by the EA and possible additional heuristics and how the [[Genetic representation#Distinction between search space and problem space|genotype-phenotype mapping]] should look like. The design of a chromosome translates these considerations into concrete data structures for which an EA then has to be selected, configured, extended, or, in the worst case, created. Finding a suitable [[Genetic representation|representation]] of the problem domain for a chromosome is an important consideration, as a good representation will make the search easier by limiting the [[Genetic representation#Distinction between search space and problem space|search space]]; similarly, a poorer representation will allow a larger search space.<ref name=ga-notes>{{cite web|title=Genetic algorithms|url=http://www.cse.unsw.edu.au/~billw/cs9414/notes/ml/05ga/05ga.html|accessdate=12 August 2015|archive-date=22 October 2019|archive-url=https://web.archive.org/web/20191022162416/http://www.cse.unsw.edu.au/~billw/cs9414/notes/ml/05ga/05ga.html|url-status=dead}}</ref> In this context, suitable [[mutation (genetic algorithm)|mutation]] and [[crossover (genetic algorithm)|crossover]] [[genetic operator|operators]]<ref name=":0" /> must also be found or newly defined to fit the chosen chromosome design. An important requirement for these operators is that they not only allow all points in the search space to be reached in principle, but also make this as easy as possible.<ref>{{Cite book |last=Rothlauf |first=Franz |url=http://link.springer.com/10.1007/978-3-642-88094-0 |title=Representations for Genetic and Evolutionary Algorithms |date=2002 |publisher=Physica-Verlag HD |isbn=978-3-642-88096-4 |series=Studies in Fuzziness and Soft Computing |volume=104 |location=Heidelberg |pages=31 |doi=10.1007/978-3-642-88094-0}}</ref><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 |series=Natural Computing Series |location=Berlin, Heidelberg |pages=49–51 |language=en |chapter=Representation and the Roles of Variation Operators |doi=10.1007/978-3-662-44874-8|s2cid=20912932 }}</ref> 

The following requirements must be met by a well-suited chromosome:

* It must allow the accessibility of all admissible points in the search space.
* Design of the chromosome in such a way that it covers only the search space and no additional areas. so that there is no [[Genetic representation#Redundancy|redundancy]] or only as little redundancy as possible.

* Observance of [[Causality conditions|strong causality]]: small changes in the chromosome should only lead to small changes in the phenotype.<ref>{{Cite book |last1=Galván-López |first1=Edgar |last2=McDermott |first2=James |last3=O'Neill |first3=Michael |last4=Brabazon |first4=Anthony |title=Proceedings of the 12th annual conference on Genetic and evolutionary computation |chapter=Towards an understanding of locality in genetic programming |date=2010-07-07 |chapter-url=https://dl.acm.org/doi/10.1145/1830483.1830646 |language=en |location=Portland Oregon USA |publisher=ACM |pages=901–908 |doi=10.1145/1830483.1830646 |isbn=978-1-4503-0072-8|s2cid=15348983 |url=https://mural.maynoothuniversity.ie/15390/1/EG_towards.pdf }}</ref> This is also called [[Genetic representation#Locality|locality]] of the relationship between search and problem space.
* Designing the chromosome in such a way that it excludes prohibited regions in the search space completely or as much as possible.

While the first requirement is indispensable, depending on the application and the EA used, one usually only has to be satisfied with fulfilling the remaining requirements as far as possible. The evolutionary search is supported and possibly considerably accelerated by a fulfillment as complete as possible.