Implementation of Genetic Algorithm
The constructor takes following keyword arguments:
populationSize: The size of the population
crossoverRate: The fraction of the population at the next generation, not including elite children, that is created by the crossover function.
mutationRate: Probability of chromosome to be mutated
epsilon: Positive integer specifies how many individuals in the current generation are guaranteed to survive to the next generation. Floating number specifies fraction of population.
selection: Selection function (default:
crossover: Crossover function (default:
mutation: Mutation function (default:
metricsis a collection of convergence metrics.
The Genetic Algorithm is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as Mutation, Crossover and Selection .