In this video, we dive into Tournament Selection—a widely used method in genetic algorithms where individuals compete in tournaments to be selected for reproduction. This technique is simple, efficient, and effective in maintaining strong selection pressure while preserving diversity.

Whether you're a beginner or advancing your knowledge in evolutionary algorithms, this tutorial will make Tournament Selection easy to understand with examples and visual explanations.

In this video:

What is Tournament Selection?

How it works and its variations

Pros and cons compared to other selection methods

Example walkthrough (with or without code)

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Tournament Selection is a Selection Strategy used for selecting the fittest candidates from the current generation in a Genetic Algorithm. These selected candidates are then passed on to the next generation. In a K-way tournament selection, we select k-individuals and run a tournament among them. Only the fittest candidate amongst those selected candidates is chosen and is passed on to the next generation. In this way many such tournaments take place and we have our final selection of candidates who move on to the next generation. It also has a parameter called the selection pressure which is a probabilistic measure of a candidate’s likelihood of participation in a tournament. If the tournament size is larger, weak candidates have a smaller chance of getting selected as it has to compete with a stronger candidate. The selection pressure parameter determines the rate of convergence of the GA. More the selection pressure more will be the Convergence rate. GAs are able to identify optimal or near-optimal solutions over a wide range of selection pressures. Tournament Selection also works for negative fitness values.

Algorithm --
1.Select k individuals from the population and perform a tournament amongst them
2.Select the best individual from the k individuals
3. Repeat process 1 and 2 until you have the desired amount of population