Evolutionary Algorithms (EAs) are a powerful class of computational methods that imitate key aspects of biological evolution—including reproduction, mutation, recombination, and selection—to find approximate solutions for complex problems where exact methods are unknown. In an EA, potential solutions are treated as "individuals" within a population, and their "fitness" or quality is determined by a specific function, guiding their evolution over successive generations. These versatile algorithms are particularly effective because they make few assumptions about the problem's underlying structure, though the computational cost of evaluating the fitness function often presents a significant challenge.

Among the various types of EAs, the Genetic Algorithm is the most widely known, primarily using string-based solutions for optimization tasks. Other notable variants include Genetic Programming, which evolves computer programs to solve computational problems, and Evolution Strategy, often employed for numerical optimization using real-numbered vectors. EAs provide robust approaches for tackling problems with dynamic or complex solution spaces by mimicking nature's problem-solving strategies.