In this video, we break down the Roulette Wheel Selection method—an essential selection technique used in genetic algorithms. Learn how this probabilistic approach mimics natural selection by giving fitter individuals a higher chance of being chosen for reproduction.

Whether you're studying evolutionary computation, working on optimization problems, or just curious about AI techniques, this tutorial will guide you step by step with clear examples.

What you'll learn:

What is Roulette Wheel Selection?

How it works in Genetic Algorithms

Probability calculations and selection process

Code demonstration (if included)

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Selection:
Selection operation is the main operation in genetic algorithm. It is used to choose the best fit individuals in the population to create the new individuals. New individuals will participate in further genetic operations to create the next generation of population. The next generation of population is created with a hope to reach the optimal solution. Many selection methods have been proposed. The most common types are :
Roulette wheel selection.
Rank selection.
Tournament selection.
Elitism.

Roulette Wheel Selection :
Roulette wheel selection is most common selection method used in genetic algorithms for selecting useful individuals (solutions) for crossover and mutation. In roulette wheel selection, as in all selection methods, possible solutions are assigned a fitness by the fitness function. This fitness level is used to associate a probability of selection with each individual.
While candidate solutions with a higher fitness will be less likely to be eliminated, poor solution may has a chance to survive in the selection process; this is an advantage, it may include some component which could prove useful following the recombination process.