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Like genetic algorithms, Particle Swarm Optimization begins with a random population of solutions in the form of individuals. (Individuals represent a set of problem values that are being optimized.) As time progresses, the individuals "swarm" generally towards the best individuals, but not directly as some randomness is involved. The best individuals are judged by a fitness function relevant to the problem, e.g., maximize the number of correct classifications or minimize the number of false negatives. For more information refer to the paper by Eberhart and Kennedy in the references.