"A genetic algorithm (GA) is a method for solving both constrained and unconstrained optimization problems based on a natural selection process that mimics biological evolution."
Genetic Algorithm (GA) with Matlab
To run the code on your computer, clone the repository and run index.m on MatLab environment.
- Jx.m -> The function to find the optimum point.
- binNumbInv.m -> This function helps you to convert binary strings into double variables (inverse function of the numbConv) for crossover and other operations.
- costF.m -> This is the cost function calculator used to assign weights for each value of the function.
- index.m -> The main method of the program. Run this file when you are ready with cloane of the repository.
- numbConv.m -> Convert double variables into binary values corresponding to the number of bits will consider in the process.
- pop -> The variable which holds the current population.
- x -> The range which independent variable is consider.
- J -> The cost function (Jx) values
- numOfPop -> Number of population consider for the algorithem. you can change and see whats happpen in the operation (but it will consume your resources)
- min_variance -> Minimum variance of the population which is the iteration terminates. by reducing this you can get accurate optimum point and it may take more itterations to converge.