Tournament selection is a method of selecting an individual from a population of individuals in a genetic algorithm. Tournament selection involves running several "tournaments" among a few individuals chosen at random from the population. The winner of each tournament (the one with the best fitness) is selected for crossover. Selection pressure is easily adjusted by changing the tournament size. If the tournament size is larger, weak individuals have a smaller chance to be selected.
Tournament selection pseudo code:
choose k (the tournament size) individuals from the population at random choose the best individual from pool/tournament with probability p choose the second best individual with probability p*(1-p) choose the third best individual with probability p*((1-p)^2) and so on...
Deterministic tournament selection selects the best individual (when p=1) in any tournament. A 1-way tournament (k=1) selection is equivalent to random selection. The chosen individual can be removed from the population that the selection is made from if desired, otherwise individuals can be selected more than once for the next generation.
Tournament selection has several benefits: it is efficient to code, works on parallel architectures and allows the selection pressure to be easily adjusted.
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- Miller, Brad L.; Goldberg, David E.. "Genetic Algorithms, Tournament Selection, and the Effects of Noise". CiteSeerX: 10.1.1.30.6625.