Selection (evolutionary algorithm)

An EA is a metaheuristic inspired by biological evolution and aims to solve challenging problems at least approximately.

Selection has a dual purpose: on the one hand, it can choose individual genomes from a population for subsequent breeding (e.g., using the crossover operator).

In addition, selection mechanisms are also used to choose candidate solutions (individuals) for the next generation.

It is a successful (slight) variant of the general process of constructing a new population.

The basis for selection is the quality of an individual, which is determined by the fitness function.

A selection procedure for breeding used early on[1] may be implemented as follows: For many problems the above algorithm might be computationally demanding.

If instead of a single pointer spun multiple times, there are multiple, equally spaced pointers on a wheel that is spun once, it is called stochastic universal sampling.

Taking the best half, third or another proportion of the individuals is truncation selection.

Other algorithms select from a restricted pool where only a certain percentage of the individuals are allowed, based on fitness value.

The higher the selection pressure, the faster a population converges against a certain solution and the search space may not be explored sufficiently.

[5][6] There is a close correlation between the population model used and a suitable selection pressure.

[5] If the pressure is too low, it must be expected that the population will not converge even after a long computing time.

[7][8] In the roulette wheel selection, the probability of choosing an individual for breeding of the next generation is proportional to its fitness, the better the fitness is, the higher chance for that individual to be chosen.

is the size of current generation (note that in this method one individual can be drawn multiple times).

Stochastic universal sampling is a development of roulette wheel selection with minimal spread and no bias.

In every generation few chromosomes are selected (good - with high fitness) for creating a new offspring.

The temperature starts out high, which means that the selection pressure is low.

The temperature is gradually lowered, which gradually increases the selection pressure, thereby allowing the GA to narrow in more closely to the best part of the search space while maintaining the appropriate degree of diversity.