The No Free Lunch Theorem: What Are its Main Implications for the Optimization Practice?

Loris Serafino ()
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Loris Serafino: Australian College of Kuwait

A chapter in Black Box Optimization, Machine Learning, and No-Free Lunch Theorems, 2021, pp 357-372 from Springer

Abstract: Abstract The chapter considers the recent but already classic theoretical result called No Free Lunch Theorem in the context of optimization practice. The No Free Lunch Theorem is probably the fundamental theoretical result of the Machine Learning field but its practical meaning and implication for practitioners facing “real life” industrial and design optimization problems are rarely addressed in the technical literature. This discussion is intended for a broad audience of mathematicians, engineers, and computer scientists and presents a probabilistic understanding of the theorem that can shed light to its meaning and impact in the industrial optimization practice.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-66515-9_12

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DOI: 10.1007/978-3-030-66515-9_12

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