The Genetic Algorithm is an optimization method based on the idea of natural evolution, inspired by Charles Darwin’s theory. Unlike simple heuristic methods that follow fixed rules, a metaheuristic method tries many possible solutions step by step to find a better result.

The Genetic Algorithm uses a fitness function to measure how good each solution is. Then, it creates new solutions using processes like selection, crossover (reproduction), and mutation. This process is repeated many times until a good or best solution is found.

One common use of the Genetic Algorithm is in clustering. It can help find better cluster centers in the k-means algorithm by reducing the distance between data points and their cluster centers. Because it can handle large and complex data, it is useful in many fields such as finance, engineering, and biology. In short, the Genetic Algorithm is a flexible and powerful method for solving complex problems.

Manual Calculation

To understand the manual calculation in the Genetic Algorithm, you can follow the steps provided in the documentation.

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