Genetic algorithm is an optimization algorithm based on natural evolution. It seeks the optimal solution by simulating genetics and mutations in the process of biological evolution.
To write a genetic algorithm using MATLAB, the following steps are required:
Define the population: First, you need to define the size of the population and the chromosomes (ie variables) of each individual.
Define the fitness function: You need to define a function to evaluate the fitness of each individual, i.e. their ability to solve the problem.
Select Parents: You need to select pairs of parents in the population for crossover.
Crossover: You need to do a crossover between the chromosomes of the parents to generate a new individual.
Mutation: You need a certain amount of mutation in the new individual to simulate the variation in the genetic process.
Select a new population: You need to select a new population to use in the next iteration.
Repeat steps 3 to 6 until the stop condition is met.
Here is a simple example that demonstrates how to implement a genetic algorithm using MATLAB:
``` % Define the population size and the chromosome length of each individual popSize = 50; chromLength = 10;
% Initialize population pop = randi([0, 1], popSize, chromLength);
% define the fitness function f