Genetic algorithms are often used to select the most suitable parents. A roulette wheel selection process is one such method. A population has n potential parents, and the roulette wheel is spun to select the best of them. The chances of an individual being selected are directly proportional to its fitness. To illustrate the process, imagine a roulette wheel with as many pockets as the number of people in the current generation. Each pocket is sized to maximize the probability of selection. The odds of an individual being selected are then multiplied by the number of slots in the roulette wheel.

Roulette wheel selection is a proportionate reproductive operator that selects an individual from a mating pool. The probability of selection increases with the fitness of the individual, so the roulette wheel selection method maintains a population’s fitness. Its benefits include the faster convergence speed and sustained population diversity. Its main drawback is that it works against the basic premise of genetic algorithms. The proposed method works by dividing the population into groups based on fitness.

A roulette wheel is a good example of a simulation of chromosome selection. Each candidate solution corresponds to a roulette wheel pocket, and the size of each pocket determines the likelihood that the candidate will be selected. In this way, selecting N chromosomes in a population is equivalent to playing N roulette games. Each candidate is drawn separately, and the resulting population contains only those with higher fitness.

Roulette wheel selection in genetic algorithms is similar to stochastic universal sampling in casino slots. The naive implementation of this algorithm generates a cumulative probability distribution (CPD) that is proportional to the fitness of an individual. When an individual’s fitness value is negative, the random selection method is useless. This is why stochastic acceptance of individuals is a more practical approach. In this case, the random selection process takes O(log n) time.

Genetic algorithms have many applications, from signal processing to artificial life. They are a powerful tool for solving many challenging problems. In particular, job-shop scheduling is NP-hard. Hence, the recent trend in job-shop scheduling is towards heuristic and metaheuristic algorithms. We have created a novel metaheuristic algorithm based on genetic algorithm that integrates a fuzzy roulette wheel selection with tabu list. The proposed algorithm was evaluated against several state-of-the-art algorithms and showed its effectiveness on 53 JSSPs.

Using a roulette wheel selection method increases the number of genetically compatible individuals and decreases the possibility of undesirable outcomes. However, in a genetic algorithm, random selection can still work for poor individuals with unfit chromosomes. Moreover, it prevents the loss of the best solution. This method is often used when the selection process is akin to a lottery. Although there are many advantages to roulette wheel selection, there are also drawbacks.