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  2. Genetic algorithm - Wikipedia

    en.wikipedia.org/wiki/Genetic_algorithm

    The basic algorithm performs crossover and mutation at the bit level. ... Examples of problems solved by genetic algorithms ... MATLAB has built in three derivative ...

  3. Crossover (evolutionary algorithm) - Wikipedia

    en.wikipedia.org/wiki/Crossover_(evolutionary...

    Crossover in evolutionary algorithms and evolutionary computation, 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 ...

  4. Genetic programming - Wikipedia

    en.wikipedia.org/wiki/Genetic_programming

    Genetic programming (GP) is an evolutionary algorithm, an artificial intelligence technique mimicking natural evolution, which operates on a population of programs. It applies the genetic operators selection according to a predefined fitness measure , mutation and crossover .

  5. Mutation (evolutionary algorithm) - Wikipedia

    en.wikipedia.org/wiki/Mutation_(evolutionary...

    The classic example of a mutation operator of a binary coded genetic algorithm (GA) involves a probability that an arbitrary bit in a genetic sequence will be flipped from its original state. A common method of implementing the mutation operator involves generating a random variable for each bit in a sequence. This random variable tells whether ...

  6. Gene expression programming - Wikipedia

    en.wikipedia.org/wiki/Gene_expression_programming

    A good overview text on evolutionary algorithms is the book "An Introduction to Genetic Algorithms" by Mitchell (1996). [4] Gene expression programming [5] belongs to the family of evolutionary algorithms and is closely related to genetic algorithms and genetic programming.

  7. Population model (evolutionary algorithm) - Wikipedia

    en.wikipedia.org/wiki/Population_model...

    When applying both population models to genetic algorithms, [5] [6] evolutionary strategy [20] [17] [21] and other EAs, [22] [23] the splitting of a total population into subpopulations usually reduces the risk of premature convergence and leads to better results overall more reliably and faster than would be expected with panmictic EAs.

  8. Edge recombination operator - Wikipedia

    en.wikipedia.org/wiki/Edge_recombination_operator

    The main application of this is for crossover in genetic algorithms when a genotype with non-repeating gene sequences is needed such as for the travelling salesman problem. It was described by Darrell Whitley and others in 1989. [1]

  9. Inheritance (genetic algorithm) - Wikipedia

    en.wikipedia.org/.../Inheritance_(genetic_algorithm)

    Download QR code; Print/export ... This process follows these basic steps: ... BoxCar 2D An interactive example of the use of a genetic algorithm to construct 2 ...