Genetic algorithm

A genetic algorithm (GA) is a search technique used in computer science to find approximate solutions to combinatorial optimization problems. Genetic algorithms are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, natural selection, and recombination (or crossover).

Related Topics:
Computer science - Combinatorial optimization - Evolutionary algorithm - Evolutionary biology - Inheritance - Mutation - Natural selection - Recombination

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Genetic algorithms are typically implemented as a computer simulation in which a population of abstract representations (called chromosomes) of candidate solutions (called individuals) to an optimization problem evolves toward better solutions. Traditionally, solutions are represented in binary as strings of 0s and 1s, but different encodings are also possible. The evolution starts from a population of completely random individuals and happens in generations. In each generation, the fitness of the whole population is evaluated, multiple individuals are stochastically selected from the current population (based on their fitness), modified (mutated or recombined) to form a new population, which becomes current in the next iteration of the algorithm.

Related Topics:
Computer simulation - Chromosomes - Candidate solutions - Fitness - Stochastically

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