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What (who) is genetic algorithms - definition
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)
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.
Wikipedia
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.
