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Genetic algorithm draws on the genetic principle in biology and is a simulationDarwinBiological evolutionnatural selection和Genetic mechanismThe computational model of the biological evolution process is a passSimulating the natural evolution process to search for the optimal solutionMethods.
Its essence is an efficient, parallel, global search method, which can automatically acquire and accumulate knowledge about the search space in the search process, and adaptively control the search process to obtain the best solution.
Since the whole logic is more complicated, you can look at "Ultra-detailed genetic algorithm analysis"
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The Genetic Algorithm is a computational model that simulates the natural evolution of Darwin's biological evolution and the evolutionary process of genetics. It is a method of searching for optimal solutions by simulating natural evolutionary processes. A genetic algorithm begins with a population that represents a potential set of solutions to a problem, while a population consists of a certain number of individuals encoded by genes.
Each individual is actually an entity with a characteristic chromosome. Chromosome as the main carrier of genetic material, that is, a collection of multiple genes, its internal representation (ie genotype) is a combination of genes that determines the external representation of the shape of the individual, such as the characteristics of black hair is controlled by the chromosome A certain combination of genes is determined by a certain combination of features. Therefore, mapping from phenotype to genotype, that is, coding work, needs to be implemented at the beginning.
Since the work of imitating gene coding is very complicated, we often simplify it, such as binary coding. After the initial generation of the population, according to the principle of survival of the fittest and the principle of survival of the fittest, evolution of generations produces better and better approximate solutions. In the first generation, the individuals are selected according to the fitness of the individual in the problem domain, and the crossover and mutation are performed by means of genetic operators of natural genetics to generate a representative new The population of the solution set. This process will result in a population of natural evolution like the descendant population is more adaptable to the environment than the previous generation, the best individual in the last generation population is decoded, which can be used as a problem to approximate the optimal solution.
In computer science and operations research, genetic algorithm (GA) is a meta heuristic inspired by the natural selection process and belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are often used to generate high quality optimization and search problem solutions that rely on bio-inspired operators such as mutation, crossover and selection. John Holland introduced the genetic algorithm based on the concept of Darwin's theory of evolution in 1960; later, his student Goldberg extended GA in 1989.