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What is Genetic Algorithm?

Genetic Algorithm (GA) is a computational model that simulates the Darwinian theory of biological evolution of natural selection and genetics mechanism of the biological evolution process, is a kind of simulation of the natural evolution process to search for the optimal solution. Its basic principle is modeled on the evolutionary law of "selection of the fittest and survival of the fittest" in the biological world.

Genetic algorithms start with a population that represents a set of potential solutions to a problem, and a population consists of a certain number of genetically coded individuals. After the first generation of the population is generated, it evolves from generation to generation to produce better and better approximate solutions according to the principles of survival of the fittest and survival of the fittest. In each generation, individuals are selected according to their fitness size in the problem domain, and combinatorial crossover and mutation are performed with the help of genetic operators of natural genetics to produce a population representing a new set of solutions.

Genetic algorithms have been widely used in the fields of combinatorial optimization, machine learning, signal processing, adaptive control and artificial life. When solving more complex combinatorial optimization problems, better optimization results can usually be obtained faster compared to some conventional optimization algorithms.

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