Editing Inheritance (genetic algorithm) (section)

{{Short description|Computer science property of heuristically modeled objects}}
{{For|information about inheritance in biology|heredity}}

In [[genetic algorithm]]s, '''inheritance''' is the ability of modeled objects to [[mating|mate]], [[mutation (genetic algorithm)|mutate]] (similar to [[biology|biological]] [[mutation]]), and propagate their problem solving [[gene]]s to the next [[generation]], in order to produce an evolved solution to a particular problem. The [[selection (genetic algorithm)|selection]] of objects that will be inherited from in each successive generation is determined by a [[fitness function]], which varies depending upon the problem being addressed.<ref name="Stuart Norvig 1995">Russell, Stuart J.; Norvig, Peter (1995). ''[[Artificial Intelligence: A Modern Approach]]''. Englewood Heights, NJ: Prentice-Hall.</ref>

The traits of these objects are passed on through [[chromosome]]s by a means similar to biological [[reproduction]]. These chromosomes are generally represented by a series of [[gene]]s, which in turn are usually represented using [[binary number]]s. This propagation of traits between generations is similar to the inheritance of [[phenotypic trait|traits]] between generations of biological [[organism]]s. This process can also be viewed as a form of [[reinforcement learning]], because the [[evolution]] of the objects is driven by the passing of traits from successful objects which can be viewed as a [[reinforcement|reward]] for their success, thereby promoting beneficial traits.<ref name="Stuart Norvig 1995" />