What are the key differences between heuristic and traditional algorithms?

Heuristics allow using past experience or good guesses in order to simply, although (probably) not optimally arrive at acceptable solutions. They have their strengths and weaknesses, strength typically being time and ease, weaknesses potentially around suboptimality, ignoring edge cases and exposure of biases. Additionally, from a production point of view having a "good enough" heuristic in place may lock the solution down and not strive to the "correct" solution.

Heuristic algorithms provide solutions faster by using practical methods and rules of thumb, especially in complex problems where traditional algorithms may not be feasible due to time or resource constraints. Traditional algorithms, on the other hand, follow a systematic, step-by-step procedure to guarantee a specific result or optimum solution. Heuristics are typically used for approximation and speed, whereas traditional methods focus on precision and definitive outcomes.

Heuristic algorithms are used to find good-enough solutions for complex problems where traditional methods maybe too slow or infeasible. These algorithms make educated guesses to speed up problem solving. An example is the A* search algorithm, which is commonly used in pathfinding and graph traversal. A* uses heuristics to estimate the cost to reach the goal from a given node, combining this with the actual cost to reach the node. This allows A* to efficiently find a path to the goal without exploring every possible route - hence this usually behaves faster than traditional pathfinding algorithms.

Traditional algorithms aims to find exact solution through comprehensive, often exhaustive procedures. An example in this space could be Dijkstra or Merge Sort, a classic sorting technique. Merge sort divides the array into halves, recursively sorts each half, and then merges the sorted halves to produce a fully sorted array. Hence, we can say that the focus on precise, optimal solution by considering all possible options.

In real-time embedded systems, where timely responses are crucial, heuristic algorithms are preferred due to their speed and adaptability. Unlike traditional algorithms, which prioritize optimality and may struggle with strict timing constraints, heuristics provide faster solutions, making them well-suited for dynamic and resource-constrained environments. While they may sacrifice guarantees on optimality, the trade-off between speed and accuracy is often acceptable in real-time applications where rapid decision-making is paramount. Therefore, heuristic algorithms are the best choice for real-time embedded systems, ensuring timely and efficient operation.

Heuristic algorithms and traditional algorithms differ in their problem-solving strategies, with the former relying on practical rules-of-thumb and the latter following systematic procedures for optimal solutions. While traditional algorithms aim for optimality, heuristic algorithms prioritize efficiency, making them suitable for complex, real-world problems with uncertain environments. Heuristic algorithms excel in navigating large solution spaces and adapting to dynamic conditions, making them preferred for very complex problems where exact solutions are impractical.