384:. Coincidentally, these "inspired algorithms" have been performing well in solving such test cases along with other complex problems, hence the high number of citations. Although, these algorithms may appear to be "metaphoric", most of the original algorithms in this field share at some level, the same level of similarity in terms of "population", "fitness", "operators", "solutions" etc. Hence, singly out "inspired" algorithms for deletion based on a few handful of publications outlining its negative "novelty" against the large number of publications outlining its "effectiveness" is still a matter up for debate. It is true that research at this point of time is mired at the metaheuristic level but till the time the scientific community decides over the debate of "fittest" vs "novelty" , as an knowledge sharing site, both the pros and cons should be weighed infront of the reader, meaning both the
447:- I don't see a real problem for Knowledge (XXG) here. There is clearly some confusion around the current fashionable flurry of "nature inspired" algorithms which is perhaps getting in the way of clarifying the underlying mathematics. But that is something the scientific community needs to sort out, not, as Ruud seems to be implying above, something that Knowledge (XXG) editors should try and sort out. Out role is to faithfully report what mainstream and reliable sources are saying. If there is some measure of confusion and dissent within that literature, then we should be reporting that as well - not trying to play God and sort it out ourselves. In other words, we should treat these algorithms on a case by case basis, and accept ones that are sufficiently cited in the literature. --
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not encourage readers to develop new algorithms such as grass, tree, tiger, penguin, snow, sky, ocean, or Hobbit algorithms. These new algorithms may only provide distractions from the solution of really challenging and truly important problems in optimization. New algorithms may be developed only if they provide truly novel ideas and really efficient techniques to solve
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Researchers have drawn various inspirations to develop a diverse range of algorithms with different degrees of success. Such diversity and success do not mean that we should focus on developing more algorithms for the sake of algorithm developments, or even worse, for the sake of publication. We do
340:- endorse for the reasons given by David Eppstein. It is not sufficient to indiscriminately delete widely used algorithms merely on the grounds that they are "nature-inspired". There may be a lot of junk offerings from some parts of the world, but there is useful work as well. --
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like the one already been put for firefly should together be put up as information. Furthermore, to clarify some of the claims but these "algorithms" have been published not only in 2nd tier journals or conferences but reputed journals like
323:. My general belief is that much of the work in this sort of metaheuristic is junk science, but the high citation counts and numbers of hits for this topic in Google Scholar make clear that, regardless of that, it is notable. —
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I could not find any respectable overview books and articles describing this work as considered relevant in the field. —
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Citations numbers of the article look superficially impressive, but include many self-citations and even reek of a
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This article is part of the following group of articles that I have all nomination for deletion (individually):
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The following discussion is an archived debate of the proposed deletion of the article below.
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Subsequent comments should be made on the appropriate discussion page (such as the article's
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himself from his book Nature-Inspired
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that have been conspicuously blanked for some algorithms due for deletion and the
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challenging problems that are not solved by existing algorithms and methods.
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The above discussion is preserved as an archive of the debate.
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to generate a more thorough discussion and clearer consensus.
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