{{Short description|Data sampling technique used in genetic algorithm}} thumb|270px|SUS example '''Stochastic universal sampling''' ('''SUS''') is a selection technique used in evolutionary algorithms for selecting potentially useful solutions for recombination. It was introduced by James Baker.<ref name="baker"> {{Cite journal | last = Baker | first = James E. | title = Reducing Bias and Inefficiency in the Selection Algorithm | journal = Proceedings of the Second International Conference on Genetic Algorithms and Their Application | pages = 14–21 | publisher = L. Erlbaum Associates | location = Hillsdale, New Jersey | year = 1987 }}</ref>
SUS is a development of fitness proportionate selection (FPS) which exhibits no bias and minimal spread. Where FPS chooses several solutions from the population by repeated random sampling, SUS uses a single random value to sample all of the solutions by choosing them at '''evenly spaced intervals'''. This gives weaker members of the population (according to their fitness) a chance to be chosen.
FPS can have bad performance when a member of the population has a really large fitness in comparison with other members. Using a comb-like ruler, SUS starts from a small random number, and chooses the next candidates from the rest of population remaining, not allowing the fittest members to saturate the candidate space.
== Pseudo Code== Described as an algorithm, pseudocode for SUS looks like:
SUS(''Population'', ''N'') ''F'' := total fitness of ''Population'' ''N'' := number of offspring to keep ''P'' := distance between the pointers (''F''/''N'') ''Start'' := random number between 0 and ''P'' ''Pointers'' := [''Start'' + ''i''*''P'' | ''i'' '''in''' [0..(''N''-1)<nowiki>]]</nowiki> '''return''' RWS(''Population'',''Pointers'') RWS(''Population'', ''Points'') ''Keep'' = [] '''for''' ''P'' '''in''' ''Points'' ''I'' := 0 '''while''' fitness sum of ''Population''[0..''I''] < ''P'' ''I''++ add ''Population''[''I''] to ''Keep'' '''return''' ''Keep''
Where <code>''Population''[''0''..''I'']</code> is the set of individuals with array-index 0 to (and including) {{mono|''I''}}.
Here RWS() describes the bulk of fitness proportionate selection (also known as "roulette wheel selection") – in true fitness proportional selection the parameter {{mono|''Points''}} is always a (sorted) list of random numbers from 0 to {{mono|''F''}}. The algorithm above is intended to be illustrative rather than canonical.
==See also== *Fitness proportionate selection *Reward-based selection
==References== <references />
Category:Selection (evolutionary algorithm)