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  2. 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 ...

  3. Genetic algorithm - Wikipedia

    en.wikipedia.org/wiki/Genetic_algorithm

    An online interactive Genetic Algorithm tutorial for a reader to practise or learn how a GA works: Learn step by step or watch global convergence in batch, change the population size, crossover rates/bounds, mutation rates/bounds and selection mechanisms, and add constraints.

  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. Gene expression programming - Wikipedia

    en.wikipedia.org/wiki/Gene_expression_programming

    Modify chromosomes using genetic operators; Go to step 5. The first four steps prepare all the ingredients that are needed for the iterative loop of the algorithm (steps 5 through 10). Of these preparative steps, the crucial one is the creation of the initial population, which is created randomly using the elements of the function and terminal ...

  6. Selection (evolutionary algorithm) - Wikipedia

    en.wikipedia.org/wiki/Selection_(genetic_algorithm)

    Selection is the stage of a genetic algorithm or more general evolutionary algorithm in which individual genomes are chosen from a population for later breeding (e.g., using the crossover operator). Selection mechanisms are also used to choose candidate solutions (individuals) for the next generation.

  7. 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 ...

  8. Evolution strategy - Wikipedia

    en.wikipedia.org/wiki/Evolution_strategy

    Often one mutation step size is used for all decision variables or each has its own step size. Mate selection to produce λ {\displaystyle \lambda } offspring is random, i.e. independent of fitness. First, new mutation step sizes are generated per mating by intermediate recombination of the parental σ j {\displaystyle {\sigma }_{j}} with ...

  9. Chromosome (evolutionary algorithm) - Wikipedia

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

    A chromosome or genotype in evolutionary algorithms (EA) is a set of parameters which define a proposed solution of the problem that the evolutionary algorithm is trying to solve. The set of all solutions, also called individuals according to the biological model, is known as the population .