It's all the same to me: revisiting rank-based probabilities and tournaments
Bryant A. Julstrom · 2003
One of the defining operations of genetic algorithms is selection: choosing chromesomes from the population to generate offspring via crossover or mutation. Researchers have described many selection algorithms, including schemes that apply probabilities based on chromosomes' ranks in the population and that simulate tournaments among chromosomes. The paper investigates two rank based assignments of probabilities: linear normalization and exponential normalization, and two tournament selection schemes: 2-tournament selection without replacement and k-tournament selection with replacement. It makes explicit the probabilities that each associates with the population's chromosomes; demonstrates, following other researchers but using elementary arguments based on these probabilities, the equivalence of linear normalization with 2-tournament selection and of exponential normalization with k-tournament selection; and argues for the use of tournament selection rather than the explicit assignment of rank based probabilities whenever possible.