Genetic algorithm facts for kids
A genetic algorithm is an algorithm that imitates the process of natural selection. They help solve optimization and search problems. Genetic algorithms are part of the bigger class of evolutionary algorithms. Genetic algorithms imitate natural biological processes, such as inheritance, mutation, selection and crossover.
The concept of genetic algorithms is a search technique often used in computer science to find complex, non-obvious solutions to algorithmic optimisation and search problems. Genetic algorithms are global search heuristics.
What is it
Natural evolution can be modeled as a game, in which the rewards for an organism that plays a good game of life are the passing on of its genetic material to its successors and its continued survival. In natural evolution, how well an individual performs depends on its who it works with and who it competes with, as well as the environment. Genetic algorithms are a simulation of natural selection on a population of candidate solutions to an optimization problem (chromosomes, individuals, creatures, or phenotypes).
Candidates are evaluated and crossbred in an attempt to make good solutions. Such solutions may take a lot of time and are not obvious to a human. An evolutionary phase is started with a population of randomly generated beings. In each generation, the fitness of every individual in the population is evaluated. Some are randomly selected from the current population (based on their fitness) and modified (recombined and possibly randomly mutated) to form a new population. The cycle repeats with this new population. The algorithm ends either after a set number of generations have passed, or a good fitness level has been reached for the population. If the algorithm has ended due to reaching a maximum number of generations, it does not necessarily mean a good fitness level has been obtained.
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