Genetic algorithms are optimization techniques inspired by natural selection, evolving solutions to complex problems by mimicking biological evolution and survival of the fittest principles.
A population of candidate solutions is created. Each solution is evaluated, and the best ones are selected to breed and create the next generation.
The main steps: selection, crossover (combining solutions), and mutation (random changes) help evolve better solutions with each generation.
They efficiently search huge solution spaces, making them ideal for problems where traditional methods struggle or get stuck in local optima.
Genetic algorithms power logistics at Amazon, optimize Tesla’s self-driving systems, and help DeepMind solve protein folding for drug discovery.
In machine learning, GAs tune hyperparameters, select features, and optimize neural network architectures for better model performance.
From robotics to finance, genetic algorithms are evolving solutions everywhere. Their adaptability makes them a key tool in AI, engineering, and beyond