There are many famous algorithms for mathematical modelling
And genetic algorithms are one of them。
The genetic algorithm has been widely used since its introduction
Especially in portfolio optimization, machine learning
Signal processing and self-adaptation control
And these areas have played a great role
Increased efficiency in solving problems。
What's a genetic algorithm
Genetic algorithms, i. E., a genetic and chromosome-based model that imitates the process of biological evolution (involving genetic, mutation, selection and cross-cutting operations) and seeks the best solution by searching。
The central idea of genetic algorithms
Genetic algorithms simulate the natural choice of darwinian biological evolution and the biological evolutionary process of genetics, which is the best way to search for the best solution by modelling natural evolutionary processes。
The central idea of genetic algorithms is “to win, to survive”. In the initial group, individuals are screened by the selected adaptive function and by means of genetic reproduction, cross-cutting and mutation, allowing individuals with a high degree of adaptability to be retained and to form new groups. The new groups received both the information of the previous generation and the better. As a result, individual adaptation in the group is increasing until certain conditions are met。
Genetic algorithms based on chromosome operations
In addressing the coding of genetic algorithms, the solution to the problem of optimization is a variable sequence called chromosomes。
There are three types of chromosome-based operations: reproduction, cross-cutting and mutation. Reproduction is a simulation of non-sexual reproduction and, when old stocks produce new stocks, the selection of more adaptable individuals is generally made, which only selects the best, but does not create new chromosomes; cross-simulates sexual reproduction in the process of biological evolution, generating new good varieties through chromosome exchange; and mutations of organisms alter their genes randomly, with little probability, in the natural genetic environment, by mutation due to a variety of incidental factors。
Exploration and application
Scientists use genetic algorithms to discover the evolution of biological diversity
The university of nanjing, professor of the nanjing geology institute of the chinese academy of sciences, and the team of shigeki students, using the supercomputer tianhu ii, and using new methods and techniques such as big data, supercalculations and genetic algorithms, have acquired an entirely new cold-blooded, complex diversity curve of marine invertebrates in the early triads, with their statistical temporal resolution ranging from 10 million years to about 26 million years, more accurately reproducing the fine process of three biocide events and two major bioradiation events in geological history。
High-speed cargo sports teams use genetic algorithms
On 23 december 2020, our country's successful development of a 350-kilometre-per-hour high-speed cargo movement team officially exited china mount motor vehicles ltd. This high-speed cargo sports group can use techniques such as large data analysis, cloud virtual distribution, precision weight control and genetic algorithms to achieve a reasonable distribution of the intelligent load of goods and vehicle loads。
