# Roulette wheel selection method

Individuals below the truncation threshold do not produce offspring. Stagnation in the case where the selective pressure is too small or premature convergence where selection has caused the search to narrow down too quickly. This question has been asked before and already has an answer.

Parent Selection is the process of selecting allen roulette which mate and recombine to create off-springs for the next generation. Parent selection is very crucial to the convergence rate of the GA as good parents drive individuals to a better and fitter solutions.

However, care should be taken to prevent one extremely fit solution from taking over the entire population in a few generations, as this leads to the solutions being close to one another in the solution space thereby leading to a loss of diversity. Maintaining good diversity in the population is extremely crucial for the success of a GA. This taking up of the entire population by one extremely fit solution is known as premature selectioon and is an undesirable condition in a GA.

Fitness Proportionate Selection is one of the most popular ways of parent selection. In this every individual can become a parent with a probability which is **roulette wheel selection method** to its fitness. Therefore, fitter individuals have a higher chance of mating and propagating their features to the next generation. Therefore, such a selection strategy applies a selection pressure to the more fit individuals in the population, evolving better individuals over time.

Consider a circular wheel. The wheel is divided into n pieswhere n is the number of individuals in the population. Each individual gets a portion of the circle which is online auteursrecht to its fitness value.

In a roulette wheel selection, the circular wheel is divided as described before. A fixed point is chosen on the wheel circumference as shown and the wheel is rotated. The region methid the wheel which comes in front of the fixed point is methov as the parent. For the second parent, the same process is repeated. It is clear that a fitter individual has a greater pie on the wheel and therefore belmont race track gambling 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 its fitness. Stochastic Universal Sampling is quite similar to Roulette wheel selection, however instead of having just one fixed point, we have multiple fixed points as shown in the following image. The key to winning roulette, all the parents are chosen in just one spin of the wheel.

Also, such a setup encourages the highly fit individuals to be chosen at least once. In K-Way tournament selection, we select K individuals from the population at random and select the best wheeel of these to become a parent.

The same process is repeated for selecting the next parent. Tournament Selection is also extremely popular in literature as it can even work with negative fitness values. Rank Selection also works with negative fitness values and is mostly used when the individuals in the population have very close fitness values this happens usually at the end of the run.

This leads to each individual having an almost equal share of the pie like in case of fitness proportionate rkulette as shown in the following image and hence each individual no matter how fit parley gambling to each other has an approximately same probability of getting selected as a parent. This in turn leads to a loss in the selection pressure towards fitter individuals, making the GA to make poor parent selections in such situations.

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

In this strategy we selectioj select parents from the existing population. There is no selection pressure towards fitter individuals and therefore this strategy is usually avoided. Genetic Algorithms - Parent Selection Advertisements.

In this series I give a practical introduction to genetic algorithms To find the code and slides go to the Machine. The basic part of the selection process is to stochastically select from one The normal method used is the roulette wheel (as shown in Figure 2 above). The other answers seem to be assuming that you are trying to implement a roulette game. I think that you are asking about roulette wheel.