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. Linear genetic programming

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

  4. Crossover (genetic algorithm)

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

  5. Evolutionary computation

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

  6. 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

  7. 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

  8. Machine learning

    have labels. Classification algorithms and regression algorithms are types of supervised learning. Classification algorithms are used when the outputs are

  9. Search-based software engineering

    engineering (SBSE) applies metaheuristic search techniques such as genetic algorithms, simulated annealing and tabu search to software engineering problems

  10. 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