In this video, we explore Rank Selection, a powerful selection method used in genetic algorithms to maintain diversity and avoid premature convergence. Unlike Roulette Wheel Selection, Rank Selection selects individuals based on their rank rather than raw fitness, ensuring a balanced evolutionary process.

Perfect for students, researchers, or developers interested in AI, machine learning, and evolutionary computation.

What you'll learn:

What is Rank Selection?

Why and when to use it

Step-by-step explanation

Comparison with other selection methods

Example walkthrough (with or without code)

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Rank Selection
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 selection) as shown in the following image and hence each individual no matter how fit relative 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.