Inheritance Genetic Algorithm: Difference between revisions
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=== 2018 === | === 2018 === | ||
* (Wikipedia, 2018) ⇒ https://en.wikipedia.org/wiki/Inheritance_(genetic_algorithm) Retrieved:2018-6-24. | * (Wikipedia, 2018) ⇒ https://en.wikipedia.org/wiki/Inheritance_(genetic_algorithm) Retrieved:2018-6-24. | ||
** 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> <P> 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. | ** 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> <P> 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. | ||
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Latest revision as of 12:23, 2 August 2022
An Inheritance Genetic Algorithm is a Genetic Algorithm that ...
- See: Reinforcement, Genetic Algorithm, Mating, Mutation (Genetic Algorithm), Biology, Mutation, Gene, Generation, Selection (Genetic Algorithm), Fitness Function, Artificial Intelligence: A Modern Approach, Chromosome.
References
2018
- (Wikipedia, 2018) ⇒ https://en.wikipedia.org/wiki/Inheritance_(genetic_algorithm) Retrieved:2018-6-24.
- In genetic algorithms, inheritance is the ability of modeled objects to mate, mutate (similar to biological mutation), and propagate their problem solving genes to the next generation, in order to produce an evolved solution to a particular problem. The 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.[1]
The traits of these objects are passed on through chromosomes by a means similar to biological reproduction. These chromosomes are generally represented by a series of genes, which in turn are usually represented using binary numbers. This propagation of traits between generations is similar to the inheritance of traits between generations of biological organisms. 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 reward for their success, thereby promoting beneficial traits.
- In genetic algorithms, inheritance is the ability of modeled objects to mate, mutate (similar to biological mutation), and propagate their problem solving genes to the next generation, in order to produce an evolved solution to a particular problem. The 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.[1]
- ↑ Russell, Stuart J.; Norvig, Peter (1995). Artificial Intelligence: A Modern Approach. Englewood Heights, NJ: Prentice-Hall.