Genetic algorithm

related topics
{math, number, function}
{specie, animal, plant}
{system, computer, user}
{theory, work, human}
{rate, high, increase}
{build, building, house}
{work, book, publish}
{household, population, female}
{city, large, area}
{acid, form, water}
{game, team, player}
{car, race, vehicle}

The genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.

Contents

Methodology

In a genetic algorithm, a population of strings (called chromosomes or the genotype of the genome), which encode candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem, evolves toward better solutions. Traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. The evolution usually starts from a population of randomly generated individuals and happens in generations. In each generation, the fitness of every individual in the population is evaluated, multiple individuals are stochastically selected from the current population (based on their fitness), and modified (recombined and possibly randomly mutated) to form a new population. The new population is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population. If the algorithm has terminated due to a maximum number of generations, a satisfactory solution may or may not have been reached.

Full article ▸

related documents
Subset sum problem
Permutation
Uniform space
Taylor series
Support vector machine
Lp space
Multiplication algorithm
Fermat number
Halting problem
Truth table
BCH code
Stochastic process
Basis (linear algebra)
Fundamental theorem of algebra
Vacuous truth
Probability theory
Hamming code
Primitive recursive function
Sorting algorithm
Monte Carlo method
Polyomino
Hyperreal number
REXX
Multivariate normal distribution
General linear group
Dynamic programming
Busy beaver
Prime number theorem
Imaginary unit
Control flow