Heuristic methods use available data, rather than predefined solutions, to solve machine and human problems. Heuristical solutions are not necessarily provable or accurate but are usually good enough to solve small-scale issues that are part of a larger problem. When a heuristic algorithm meets a new crossroad, a decision is made and learned. Successive iteration results are interdependent, as each level learns which avenues to choose and discard, based on its proximity to the solution. Thus, because some possibilities are less likely to reach a viable solution, they are never generated.
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