can not distinguish.
By a mere linguistic definition, a heuristic is a process involving or serving as an aid to learning, discovery, or problem-solving by experimental and especially trial-and-error methods <heuristic techniques> <a heuristic assumption>; also : of or relating to exploratory problem-solving techniques that utilize self-educating techniques (as the evaluation of feedback) to improve performance <a heuristic computer program>.
Meta-heuristics on the other hand simply provide a framework beyond the experimental problem solving methods. Hence the term "meta", i.e. beyond in Greek. Meta-heuristics mainly involve the parallel probabilistic (can be changed based on the internal fine tuning of the algorithms parameters) exploitation and exploration of the solution space in order to search for sub-optimal solutions. They do provide a means to realise the probabilistic escape of local minima, a feature not catered for (at least in the generic default sense) in traditional heuristic algorithms.
Hope that brings some new insights on the very well written information of our previous colleagues in this thread.
I find this discussion very interesting. I totally agree with that heuristics are typically only suited for a very specific problem. On the other hand, metaheuristics are more generally applicable to a wider range of problems.
I do still have the following question. Is it a general characteristic of heuristics that these are greedy, and thereby generally get stuck in local optima? Whereas metaheuristics are in general less greedy and can escape from local optima. Or is this only true for some heuristics/metaheuristics?
University of Gezira
Wayne State University
Delft University of Technology
Vijay Kumar Mishra
Indian Institute of Technology Guwahati
Institute for Development & Research in Banking Technology
Université Constantine 2
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