Information about Test

  1. Genetic algorithm

    a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA)

  2. Genetic programming

    intelligence, genetic programming (GP) is a technique of evolving programs, starting from a population of unfit (usually random) programs, fit for a particular

  3. Crossover (genetic algorithm)

    In genetic algorithms and evolutionary computation, crossover, also called recombination, is a genetic operator used to combine the genetic information

  4. Chromosome (genetic algorithm)

    In genetic algorithms, a chromosome (also sometimes called a genotype) is a set of parameters which define a proposed solution to the problem that the

  5. Selection (genetic algorithm)

    Selection is the stage of a genetic algorithm in which individual genomes are chosen from a population for later breeding (using the crossover operator)

  6. Mutation (genetic algorithm)

    Mutation is a genetic operator used to maintain genetic diversity from one generation of a population of genetic algorithm chromosomes to the next. It

  7. Evolutionary computation

    programming, evolution strategies, genetic algorithms, and genetic programming as sub-areas. Simulations of evolution using evolutionary algorithms and

  8. Schema (genetic algorithms)

    A schema is a template in computer science used in the field of genetic algorithms that identifies a subset of strings with similarities at certain string

  9. Linear genetic programming

    "Linear genetic programming" is unrelated to "linear programming". Linear genetic programming (LGP) is a particular subset of genetic programming wherein

  10. Genetic fuzzy systems

    Genetic fuzzy systems are fuzzy systems constructed by using genetic algorithms or genetic programming, which mimic the process of natural evolution, to