The basic principle of genetic algorithm, genetic algorithm to solve what problem.

Genetic algorithm is a computational model that simulates the natural selection of Darwinian biological evolution and the biological evolution of genetic mechanisms, and is a method to search for optimal solutions by simulating the process of natural evolution.
so today's small editor for you to introduce the basic principles of genetic algorithm, genetic algorithm to solve what problems.
genetic algorithm basic principle genetic algorithm (GA) is a meta-heuristic natural selection process, belonging to the evolutionary algorithm (EA) category.
genetic algorithms are often solutions to high-quality optimization and search problems that use bio-inspired operators such as variation, crossover, and selection.
drawing on the theory of biological evolution, the genetic algorithm simulates the problem as a biological evolution process, through the operation of genetics, cross, mutation, natural selection and so on to produce the next generation of solutions, and gradually eliminate the solution of low adaptive function value, increase the solution of high adaptive function.
this evolution of Generation N is likely to evolve individuals with high adaptive function values.
genetic algorithm to solve what problemS Because the whole search strategy and optimization search method of genetic algorithm do not rely on gradient information or other auxiliary knowledge in the calculation, but only need to affect the search direction of the target function and the corresponding adaptation function, so the genetic algorithm provides a common framework to solve the complex system problem, it does not depend on the specific area of the problem, the type of problem has a strong robust, so it is widely used in many sciences, so it is widely used in many sciences, Here's a look at some of the main application areas of genetic algorithms: function optimization function optimization is a classic application field of genetic algorithm, but also a common example of genetic algorithm performance evaluation, many people have constructed a variety of complex forms of test functions: continuous and discrete functions, convex and concave functions, low-dimensional functions and high-dimensional functions, single-peak functions and multi-peak functions.
for some nonlinear, multi-model, multi-objective function optimization problems, using other optimization methods is more difficult to solve, and genetic algorithm can easily get better results.
combination optimization with the increase of problem scale, the search space of combination optimization problem also increases sharply, sometimes in the current calculation with enumeration method is difficult to find the optimal solution.
of this complex problem, people have realized that the main focus should be on finding satisfactory solutions, and genetic algorithms are one of the best tools for finding such satisfactory solutions.
practice has proved that genetic algorithm is very effective for NP problem in combination optimization.
genetic algorithms have been successfully applied in solving traveler problems, backpacking problems, boxing problems, graphing problems, etc.
, GA has also been widely used in production scheduling problems, automatic control, robotics, image processing, artificial life, genetic coding and machine learning.
workshop scheduling problem is a typical NP-Hard problem, genetic algorithm as a classic intelligent algorithm is widely used in workshop scheduling, many scholars are committed to using genetic algorithm to solve the problem of workshop scheduling, and now has achieved very fruitful results.
from the initial traditional workshop scheduling (JSP) problem to the flexible workshop scheduling problem (FJSP), the genetic algorithm has excellent performance, in many studies have been optimal or near excellent solution.
genetic algorithms are widely used to solve the problems of numerical optimization, combination optimization, machine learning, intelligent control, artificial life, image processing, pattern recognition and so on.
more specific are: the most valuable function problem, the traveler problem, the backpack problem, the vehicle path problem, the production schedule problem, the location problem, etc.
above is the basic principle of genetic algorithm brought by everyone, genetic algorithm to solve what problems, hope to provide you with reference help.
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