Difference between revisions of "Genetic algorithms"
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| − | A '''genetic algorithm''' is a [[stochastic]] numerical algorithm used to solve problems where [[analytic]] or step-wise | + | A '''genetic algorithm''' is a [[stochastic]] numerical algorithm used to solve optimization problems where [[analytic]] or step-wise optimization is impossible or intractable. Genetic algorithms are a popular subset of [[Evolutionary algorithm|Evolutionary algorithms]] which use common concepts in [[evolutionary biology]] and principles of the [[Theory of evolution]] such as: [[inheritance]], [[mutation]], [[selection]], and [[recombination]]. |
| − | Solutions to a problem are randomly generated and then allowed to interact in a modeled environment. Various solutions can [[breed]] with each other producing new combinations, also at random intervals certain elements of the solution can randomly mutate. The | + | Solutions to a problem are randomly generated and then allowed to interact in a modeled environment. Various solutions can [[breed]] with each other producing new combinations, also at random intervals certain elements of the solution can randomly mutate. The fitness of any given solution to the problem is measured by a fitness function and those that are a better fit are selected for. This can either happen by increasing the rate of offspring reproduction in better solutions or by killing off solutions that perform worse. |
| − | Genetic algorithms are used in a wide subset of disciplines including: [[biology]], [[engineering]], [[medicine]] and [[chemistry]]. | + | Genetic algorithms are used in a wide subset of disciplines including: [[biology]], [[engineering]], [[medicine]] and [[chemistry]]. |
| − | ==See | + | '''Genetic programming''' is a type of genetic algorithm where the population consists of programs whose fitness is determined by their ability to solve a given problem. |
| − | [[Neural networks]] | + | |
| + | ==See also== | ||
| + | [[Monte Carlo method]] | ||
| + | |||
| + | [[Neural networks]] | ||
| + | |||
| + | [[Evolutionary algorithm]] | ||
==References== | ==References== | ||
| − | Schmitt, Lothar M (2001), Theory of Genetic Algorithms, Theoretical Computer Science (259), pp. | + | Schmitt, Lothar M (2001), Theory of Genetic Algorithms, Theoretical Computer Science (259), pp. 1–61 |
| − | [[ | + | |
| + | ==External links== | ||
| + | *[http://www.talkorigins.org/faqs/genalg/genalg.html "Genetic Algorithms and Evolutionary Computation" on the TalkOrigins Archive] | ||
| + | |||
| + | [[Category:Mathematics]] | ||
| + | [[Category:Computer Science]] | ||
Latest revision as of 00:56, July 25, 2020
A genetic algorithm is a stochastic numerical algorithm used to solve optimization problems where analytic or step-wise optimization is impossible or intractable. Genetic algorithms are a popular subset of Evolutionary algorithms which use common concepts in evolutionary biology and principles of the Theory of evolution such as: inheritance, mutation, selection, and recombination.
Solutions to a problem are randomly generated and then allowed to interact in a modeled environment. Various solutions can breed with each other producing new combinations, also at random intervals certain elements of the solution can randomly mutate. The fitness of any given solution to the problem is measured by a fitness function and those that are a better fit are selected for. This can either happen by increasing the rate of offspring reproduction in better solutions or by killing off solutions that perform worse.
Genetic algorithms are used in a wide subset of disciplines including: biology, engineering, medicine and chemistry.
Genetic programming is a type of genetic algorithm where the population consists of programs whose fitness is determined by their ability to solve a given problem.
See also
References
Schmitt, Lothar M (2001), Theory of Genetic Algorithms, Theoretical Computer Science (259), pp. 1–61
