Genetic Algorithm

CORE DEFINITION

Genetic Algorithm (GA) is a search algorithm in computational mathematics used for solving optimization problems, and is a type of evolutionary algorithm. Evolutionary algorithms were originally developed by drawing on phenomena in evolutionary biology, including heredity, mutation, natural selection, and crossover. Genetic algorithms are typically implemented as computer simulations. For an optimization problem, a certain number of candidate solutions (called individuals) can be abstractly represented as chromosomes, allowing the population to evolve toward better solutions. Traditionally, solutions are represented in binary (i.e., strings of 0s and 1s), but other representations are also possible. Evolution starts from a population of completely random individuals and proceeds in generations. In each generation, the fitness of the entire population is evaluated, multiple individuals are randomly selected from the current population (based on their fitness), and a new population is produced through natural selection and mutation, which becomes the current population in the next iteration of the algorithm.