Chromosome (evolutionary algorithm): Difference between revisions

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In [[genetic algorithm]]s, the terma '''chromosome''' is(also usedsometimes tocalled describea any'''genome''') oneis a set of theparameters possiblewhich solutionsdefine a proposed solution to the taskproblem that the particular genetic algorithm has beenis designedtrying to solve. AThe GAschromosome '''chromosomes'''is representoften arepresented populationas ofa solutionssimple to[[string#Mathematics theand problem.Computer Science|string]], Thealthough termsa arewide usedvariety inof analogy to biologicalother [[chromosomedata structure]]s are also in use as chromosomes.

A genetic algorithm creates many chromosomes, either randomly or by design, as an initial population. These chromosomes are each evaluated by the [[fitness function]], which ranks them according to how good their solution is. The chromosomes which produced the best solutions, relatively speaking within the population, are allowed to breed, called [[crossover (genetic algorithm)|crossover]]. The best chromosomes' data is mixed, hopefully producing a better next generation.

Chromosomes are represented as a list of parameters; most commonly this is a string of 0s and 1s, though other representations are also used. The set of chromosomes is operated upon using the [[genetic operator]]s to refine the population from one generation to the next.

==SeeData alsoTypes==

Different data types have proven better suited to different problem domains.

''Please add to this; list data types and problem domains which they are particularly applicable to.''

==See also==

* [[Chromosome|Chromosome (biology)]]