1. The concept of genetic algorithm
Genetic algorithm is a random global search and optimization method developed by imitating the biological evolution mechanism in nature. It draws on Darwin's theory of evolution and Mendel's theory of genetics. Its essence is an efficient, parallel, global search method.
2. Characteristics of Genetic Algorithms
2.1 Advantages of Genetic Algorithms
(1) The representation of feasible solutions is extensive
(2) It has better global search performance, making the genetic algorithm itself easy to parallelize
(3) No auxiliary information is required
(4) Intrinsic heuristic random search feature
(5) It is not easy to fall into the local optimum during the search process, and even when the defined fitness function is discontinuous, irregular or noisy, the global optimum solution can be found with a high probability
(6) The genetic algorithm uses the natural evolution mechanism to express complex phenomena, and can quickly and reliably solve very difficult problems.
(7) Genetic algorithm has fixed parallelism and parallel computing ability
(8) Genetic algorithm is scalable and easy to mix with other technologies
2.2 Disadvantages of Genetic Algorithms
(1) Irregular coding and uncertainty of coding
(2) A single genetic algorithm code cannot fully express the constraints of the optimization problem. One way to consider constraints is to use thresholds for infeasible solutions, so that computation time must increase
(3) The efficiency is lower than other traditional optimization methods
(4) prone to premature convergence
(5) There is no effective quantitative analysis method for the accuracy, reliability and computational complexity of the algorithm.
1.3 Basic Terms of Genetic Algorithms
3. Genetic algorithm operation process
4. For practical problems, the operation flow of the genetic algorithm with better effect is as follows:
Notice:
Selection operation is generally roulette selection
The crossover operation is generally a single-point crossover
The mutation operation is generally a basic bit mutation
Population size is generally 20-100
The number of iterations is generally 100-500
Crossover probability is generally 0.4-0.99
The probability of mutation is generally 0.001%-0.1%
Increased probability of mutation prevents precocious puberty
The crossover operation determines the global search ability of the genetic algorithm
The mutation operation determines the local search ability of the genetic algorithm
5. Reference procedure:
Genetic algorithm MATLAB source program for simple function optimization Download address: https://pan.baidu.com/s/1HEh-PqCQtZxwslfUWUbBWg