Genetic algorithms: Difference between revisions
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The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that is based on natural selection, the process that drives biological evolution. The genetic algorithm repeatedly modifies a population of individual solutions. At each step, the genetic algorithm selects individuals at random from the current population to be parents and uses them to produce the children for the next generation. Over successive generations, the population "evolves" toward an optimal solution. You can apply the genetic algorithm to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective function is discontinuous, nondifferentiable, stochastic, or highly nonlinear. The genetic algorithm can address problems of mixed integer programming, where some components are restricted to be integer-valued.<ref>https://www.mathworks.com/help/gads/what-is-the-genetic-algorithm.html</ref> | |||
=== Initial population === | === Initial population === | ||
=== Fitness function === | === Fitness function === | ||
=== Selection === | === Selection === | ||
Selection rules select the individuals, called parents, that contribute to the population at the next generation. | |||
=== Crossover === | === Crossover === | ||
Crossover rules combine two parents to form children for the next generation. | |||
=== Mutation === | === Mutation === | ||
Mutation rules apply random changes to individual parents to form children. | |||
== Standards == | == Standards == | ||
* | * Outline the use of genetic algorithms. | ||
== References == | == References == | ||
Revision as of 21:56, 3 February 2019
The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that is based on natural selection, the process that drives biological evolution. The genetic algorithm repeatedly modifies a population of individual solutions. At each step, the genetic algorithm selects individuals at random from the current population to be parents and uses them to produce the children for the next generation. Over successive generations, the population "evolves" toward an optimal solution. You can apply the genetic algorithm to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective function is discontinuous, nondifferentiable, stochastic, or highly nonlinear. The genetic algorithm can address problems of mixed integer programming, where some components are restricted to be integer-valued.[2]
Initial population[edit]
Fitness function[edit]
Selection[edit]
Selection rules select the individuals, called parents, that contribute to the population at the next generation.
Crossover[edit]
Crossover rules combine two parents to form children for the next generation.
Mutation[edit]
Mutation rules apply random changes to individual parents to form children.
Standards[edit]
- Outline the use of genetic algorithms.