genetic algorithms - définition. Qu'est-ce que genetic algorithms
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Qu'est-ce (qui) est genetic algorithms - définition

COMPETITIVE ALGORITHM FOR SEARCHING A PROBLEM SPACE
Genetic algorithms; Genetic Algorithm; Genetic Algorithms; Building block hypothesis; Speciation (genetic algorithm); Darwinian algorithm; GATTO; Theory of genetic algorithms; GEGA; Genethc algorithm; Parallel genetic algorithms; Optimization using genetic algorithms; Adaptive genetic algorithms; Applications of genetic algorithms; History of genetic algorithms

genetic algorithms         
genetic algorithm         
(GA) An evolutionary algorithm which generates each individual from some encoded form known as a "chromosome" or "genome". Chromosomes are combined or mutated to breed new individuals. "Crossover", the kind of recombination of chromosomes found in sexual reproduction in nature, is often also used in GAs. Here, an offspring's chromosome is created by joining segments choosen alternately from each of two parents' chromosomes which are of fixed length. GAs are useful for multidimensional optimisation problems in which the chromosome can encode the values for the different variables being optimised. {Illinois Genetic Algorithms Laboratory (http://GAL4.GE.UIUC.EDU/illigal.home.html)} (IlliGAL). (1995-02-03)
Crossover (genetic algorithm)         
  • Example of a discrete recombination in the three-dimensional case. The two possible offspring lie on the corners of the cuboid marked in blue.
  • In the two-dimensional case, the two offspring of discrete recombination lie on the corners marked in blue, while the entire gray area is in question for the offspring of intermediate recombination.
  • TwoPointCrossover.svg
OPERATOR USED TO VARY THE PROGRAMMING OF CHROMOSOMES FROM ONE GENERATION TO THE NEXT
Recombination (genetic algorithm)
In genetic algorithms and evolutionary computation, crossover, also called recombination, is a genetic operator used to combine the genetic information of two parents to generate new offspring. It is one way to stochastically generate new solutions from an existing population, and is analogous to the crossover that happens during sexual reproduction in biology.

Wikipédia

Genetic algorithm

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, etc.