If you’ve ever wondered how nature could inspire computers, the Genetic Algorithm (GA) is a perfect example. Borrowing the idea of survival of the fittest from Darwin’s theory, GAs help machines “evolve” better solutions over time.
In machine learning, a genetic algorithm is used to optimize models and parameters. It starts by creating many random solutions, checks how good they are using a fitness function, and then combines the best ones. This process repeats, gradually improving the results — much like evolution in the wild.
Here’s how it works step by step:
Initialization: Start with a set of random possible solutions.
Fitness Evaluation: Score each solution based on how well it performs.
Selection: Pick the top performers.
Crossover: Mix two strong solutions to create a new one.
Mutation: Make small random tweaks to introduce variety.
This balance between exploration (mutation) and exploitation (crossover) helps avoid getting stuck with poor results and leads to better optimization.
Developers use genetic algorithms for feature selection, hyperparameter tuning, and even neural network training. They’re powerful when traditional algorithms fail to handle complex or non-linear problems.
Yes, GAs can be slow and computationally heavy — but the results are often worth it.
If you’re exploring AI, machine learning, or data science, learning about genetic algorithms can give you a deeper understanding of how optimization truly works.
Check out Ze Learning Labb for beginner-friendly courses that explain these concepts with real-world examples.
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