The basic genetic algorithm is a search algorithm based on the mechanism of natural selection and genetics. It starts with a population of candidate solutions represented as individuals (chromosomes) in a population. Each individual is evaluated by a fitness function that measures how well the individual solves the problem.
The algorithm then creates a new generation of individuals by applying the following genetic operators:
Selection: The fittest individuals are selected from the current population to form the next generation.
Crossover: Two selected individuals are chosen, and a crossover point is selected. The genes before the crossover point are swapped to create two new individuals.
Mutation: A small random change is introduced into an individual to prevent the population from converging too quickly.
The new generation is then evaluated by the fitness function, and the process is repeated until a stopping criterion is met, such as reaching a certain number of generations or achieving a satisfactory fitness level.
The genetic algorithm can be used for a wide range of optimization problems, including function optimization, parameter tuning, and feature selection. It is also used in machine learning to optimize the parameters of a model.
