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Iterative Optimizers - Difficulty Measures and Benchmarks (Hardcover)
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Iterative Optimizers - Difficulty Measures and Benchmarks (Hardcover)
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Almost every month, a new optimization algorithm is proposed, often
accompanied by the claim that it is superior to all those that came
before it. However, this claim is generally based on the
algorithm's performance on a specific set of test cases, which are
not necessarily representative of the types of problems the
algorithm will face in real life. This book presents the
theoretical analysis and practical methods (along with source
codes) necessary to estimate the difficulty of problems in a test
set, as well as to build bespoke test sets consisting of problems
with varied difficulties. The book formally establishes a typology
of optimization problems, from which a reliable test set can be
deduced. At the same time, it highlights how classic test sets are
skewed in favor of different classes of problems, and how, as a
result, optimizers that have performed well on test problems may
perform poorly in real life scenarios.
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