Genetic algorithms are ideal for optimization problems with large search spaces and few feasible solutions. They are adaptive heuristic search algorithms inspired by Darwinian evolution, using techniques like selection of the fittest solutions, crossover of solution features, and random mutation over generations to evolve improved solutions. Key steps include initializing a population, evaluating fitness, selecting parents, applying genetic operators, and repeating until termination criteria are met. Parameter tuning, such as population size and mutation rate, affects performance but is challenging.