When and Why Metaheuristics Researchers can Ignore ``No Free Lunch'' Theorems
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- @Article{McDermott:2020:SNCS,
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author = "James McDermott",
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title = "When and Why Metaheuristics Researchers can Ignore
``No Free Lunch'' Theorems",
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journal = "SN Computer Science",
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year = "2020",
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volume = "1",
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pages = "60",
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keywords = "genetic algorithms, genetic programming,
Metaheuristics, No free lunch, NFL, Evolutionary
computation, Problem domain, Anthropic principle",
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ISSN = "2661-8907",
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URL = "http://arxiv.org/abs/1906.03280",
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DOI = "doi:https://doi.org/10.1007/s42979-020-0063-3",
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abstract = "The No Free Lunch (NFL) theorem for search and
optimisation states that averaged across all possible
objective functions on a fixed search space, all search
algorithms perform equally well. Several refined
versions of the theorem find a similar outcome when
averaging across smaller sets of functions. We argue
that NFL results continue to be misunderstood by many
researchers, and addresses this issue in several ways.
Existing arguments against real-world implications of
NFL results are collected and re-stated for
accessibility, and new ones are added. Specific
misunderstandings extant in the literature are
identified, with speculation as to how they may have
arisen. This paper presents an argument against a
common paraphrase of NFL findings. That algorithms must
be specialised to problem domains in order to do well.
After problematising the usually undefined term domain.
It provides novel concrete counter-examples
illustrating cases where NFL theorems do not apply. In
conclusion it offers a novel view of the real meaning
of NFL, incorporating the anthropic principle and
justifying the position that in many common situations
researchers can ignore NFL.",
- }
Genetic Programming entries for
James McDermott
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