Each generation in the genetic algorithm is the set of all current solutions. In each generation of the genetic algorithm, two-parent chromosomes (encodings) are selected based on their fitness values which are determined by the fitness function; these chromosomes are used by the mutation and crossover operators to produce two offspring chromosomes for the new population. Each encoding is an argument to the fitness function and outputs a score. In the real world, we can imagine a fitness function being an organism's ability to survive and the score being the ease at which it does so. This determines how "good" a genetic encoding is with respect to the fitness function. Not all solutions pass the doorstep and are therefore not considered for the next step.
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