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Overview There are many selection operators (or methods) for evolutionary algorithms. These selection methods differ in the way they evaluate the individuals given their fitness or how they compute their fitness in the first place. Some selection operators select individuals deterministically, while others select them stochastically, which can prevent ...


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There are multiple ways to interpret those steps. The most common standard approaches are select two parents and produce two offspring; repeat until child population is the same size as parent population, and let the children replace their parents unconditionally (generational GA) same as the above, but allow a few parents to live on instead of a few ...


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As originally conceived in James Baker's 1989 paper Reducing bias and inefficiency in the selection algorithm, Stochastic Universal Sampling accepts a population containing $N$ individuals, and a number of parents to sample, denoted $n$. Assuming fitness values are normalized so that they sum up to $N$, at each step, a new pointer is placed a step equal in ...


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Yes, you select the same individual multiple times, according to the distribution of fitness values. Typically, you have at least as many offspring as parents, so if you didn't replace parents in the pool to be potentially selected again, you'd just have every parent being selected once, which defeats the purpose of selection. You want fitter parents to be ...


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It is not true that the number of solutions necessarily decreases during the selection phase (if by solutions you mean the number of individuals in the population). The number of solutions is usually constant, i.e., you can start with $N$ individuals, then, every iteration (or generation), you can e.g. select two individuals from the population (typically, ...


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First of all, for a lot of realistic problems, the fitness function evaluation is usually orders of magnitude greater in complexity than the rest of the genetic algorithm. This is not always true, but often is true (e.g. imagine trying to optimise a simulation where you need to execute the simulation completely to obtain the fitness). So optimising the GA ...


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This is a more complex question than it might initially seem. A genetic algorithm models a biological process,namely population genetics. No biological population evolves to a single cloned individual, a process in genetic algorithms referred to as premature convergence where the population converges to a single non optimal, though possibly locally optimal, ...


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