The term "heuristics" comes from the Greek word "heuriskein," which means "to find" or "to discover." This term reflects the idea of finding or discovering solutions through intuitive or trial-and-error methods.
Mathematico-logical heuristics involve using structured mathematical or logical methods to solve problems. They include techniques like calculus for optimizing functions, linear programming for maximizing or minimizing linear objectives under constraints, and 3-SAT for solving complex logical puzzles. These heuristics apply rigorous mathematical rules and logic to break down and solve problems step-by-step. They are especially useful for structured problems where precise, logical solutions are needed, like in operations research, computer science, and engineering.
Nature-inspired heuristics are problem-solving methods modeled after natural processes. Like how birds flock or bees forage, these algorithms mimic nature to tackle complex problems. They use strategies like evolution, ant colony behavior, or bird flocking to find good solutions, blending randomness with specific rules from nature. These methods are useful for tough problems where traditional approaches might fail, creatively applying nature's wisdom to areas like computer science, engineering, and logistics to find efficient, often surprising, solutions.
Human heuristics are simple, intuitive rules we use to make quick decisions, like "avoid dark alleys at night." They are based on our experiences and common sense, helping us navigate everyday choices efficiently without much thought. Useful in fast-paced or uncertain situations, these shortcuts can lead to good enough decisions. However, they're not always reliable for complex, critical decisions or in unfamiliar contexts, as they can oversimplify situations and be influenced by biases, potentially leading to poor choices.