Eventually, over many generations, a GA can produce candidates that approximately solve the optimization problem. Some of the new candidates experience mutations. The better candidates are combined to create a new set of candidates. Candidates are evaluated for "fitness" by plugging them into the objective function. A genetic algorithm tries to mimic natural selection and evolution by starting with a population of random candidates. Genetic algorithms are heuristic methods that can be used to solve problems that are difficult to solve by using standard discrete or calculus-based optimization methods. Some programmers love using genetic algorithms. It discusses choices that you must make when you implement these operations. It discusses two operators (mutation and crossover) that are important in implementing a genetic algorithm. This article uses an example to introduce to genetic algorithms (GAs) for optimization.
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