A genetic algorithm is a metaheuristic inspired by Darwin's evolutionism.
Genetic algorithms are used to find a close-to-optimal solution among a pool of candidate solutions. They are almost ubiquitous in optimization problems, with applications ranging from natural sciences to finance.
An application in a quantum information context can be found at nature
The algorithm proceeds as follows:
- Randomly initiate a population of candidate solutions
- Perform crossing-over between candidates
- Perform mutations
- Evaluating the fitness of the candidates
- Select the best individuals
- Repeat until convergence has reached
A genetic algorithm for finding the minimum/maximum of a function
A genetic algorithm able to learn an image!