Table 1.

A list of concepts in both fields and their translation between the fields.

PG	EC	Meaning
Neutrality	Uniform selection	All individuals in the target population are equally likely to be selected into the next generation. This is equivalent to no selection or what is called random drift in PG
–	Drift	The change in expectation of some quantity over the stochastic process. It is typically the expected advance of the algorithm, conditional on the current state
Genetic drift	Genetic drift	It is typically meant to refer to the stochasticity associated with sampling from finite populations
Unlinked genes	Uniform crossover	A recombination pattern in which the probability of inheriting the gene copy from any of the parents is 1/2 and does not depend on its position in the genome
Selection coefficient	Reproduction rate	The relative growth advantage of an allele or genotype over the mean of the population. It is formally defined as $s=\frac{W_i-\overline{W}}{\overline{W}}$. It is related to the “reproduction rate” concept in EC. In our framework, this is a quantity derived from the particular selection scheme imposed to the population. As can be seen from the formal definition, it depends on the current composition of the population
Overlapping generation models	Elitist algorithms	Models in which the population at the next time step (iteration) is selected from the combined pool of parents and offspring. In PG this is termed iteroparity. These are termed elitist because when used in conjunctions with cut selection (+-selection) this guarantees that the best individual is always kept
Non-overlapping (or discrete) generation models	Generational algorithms	Models in which the population at the next time step (iteration) is selected solely from the offspring (which may be exact copies of the parents) of the parents. In PG this is termed semelparity.