# Roulette Selection in Genetic Algorithm | Royal Palace

Fitness proportionate selection, also known as roulette wheel selection, is a genetic operator used in genetic algorithms to select potentially practical recombination solutions.

As in all selection methods, the fitness function assigns a fitness to a possible solution or chromosome in proportionate fitness selection. This fitness level is used to associate a probability of selection with each chromosome.Â

Therefore, fitter individuals have a higher chance of mating and propagating their features to the Next Generation. Therefore, such a selection strategy applies selection pressure to the more fit individuals in the population, evolving better individuals over other coming generations.

It could be imagined similar to the roulette wheel in a casino. Usually, a proportion of the wheel is assigned to each possible selection based on the fitness value. One can easily achieve it by dividing the fitness of a selection from all the selections, thereby normalizing them to 1. Then, a random selection is made similar to how the roulette wheel is rotated.

At the same time, the candidate solution with a higher fitness will be less likely to be eliminated because their probability of selection is less than (1 or 100%). Contrast this with a less sophisticated selection algorithm such as truncation selection, which will eliminate a fixed percentage of the weakest candidate with low fitness.

Â It is not zero, which means it still survives. It is an advantage because there is a chance that even a weak solution may have some features or characteristics which could be helpful.

## Fitness proportionate selectionÂ

It is one of the most popular ways of parent selection. In this way, every individual can become a parent with a probability proportional to their fitness. Therefore fitter individuals have a higher chance of mating and propagating their features to the Next Generation. Thus such a selection strategy applies selection pressure to the more fit individuals in the population.Â

Suppose you consider a wheel. It is divided into pieces, where “n” is the number of individuals in the population. Each individual gets a portion of the circle, proportional to its fitness fits.

Two implementations of proportionate fitness selection are as follows-Â

## Roulette wheel selection –

Â The circular wheel had different part divisions described before in a roulette wheel selection. A fixed point is chosen on the wheel circumference as shown, and the wheel is rotated. The wheel region that comes in front of the fixed wheel is selected as the parent.Â

Also, the fitter individual has a more excellent pie on the wheel. Therefore, it simply means a greater chance of landing in front of the fixed point when the wheel is rotated. Therefore the probability of choosing an individual depends directly on their fitness.

## Stochastic universal sampling

Universal sampling is quite similar to roulette wheel selection; however, we have multiple fixed points instead of just one fixed point. Therefore all the parents are chosen in just one spin of the wheel.

## Tournament selectionÂ

In k way tournament selection, we select k individuals from the population at random and select the best to become a parent. The exact process is repeated for choosing the following parents. Tournament selection is also trendy in literature as it can even work with negative fitness values.

## Rank selectionÂ

The rank selection also works with the negative fitness values and is mainly used when the population has very relative values. It led to each individual having an almost equal share of the pie. No matter how to fit relative to each other, every individual has the approximately same probability of getting selected as a parent; this, in turn, leads to a loss in the selection pressure toward fitter individuals making the ga to make poor parent selection in such situations.

We remove the concept of a fitness value while selecting a parent. However, every individual in the population is ranked according to their fitness value while selecting parents. The selection of the parents depends on the rank of each individual and not the fitness. The higher-ranked individual is preferred more than the lower-ranked ounces.

## Random selectionÂ

In this strategy, we select random patents from the existing population. There is no selection pressure toward fitter individuals, and therefore this strategy is usually avoided.

### ConclusionÂ

In this article, we study the different genetic algorithms about how the genetic algorithm is a search-based algorithm based on natural selection and genetic algorithm selection. They are randomized in nature and better in selection. They are more fruitful and able to silver a good enough solution quickly. It makes genetic algorithms attractive to use in solving optimization problems.Â

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