Genetic algorithms are a type of optimization technique inspired by the process of natural selection. They work by creating a population of potential solutions to a problem and then evolving these solutions over time. Each solution is represented as a set of parameters, similar to genes, and the best solutions are selected based on their performance. Through processes like selection, crossover, and mutation, genetic algorithms iteratively improve the population. The goal is to find the most effective solution by mimicking the way nature evolves species, allowing for exploration of a wide range of possibilities in complex problem-solving scenarios.