AI Article Synopsis
- This text discusses advanced concepts in black box search algorithms, extending prior findings related to the No Free Lunch (NFL) theorem.
- It introduces new theoretical tools that apply when functions and algorithms are limited to specific benchmarks or collections, which may not follow typical permutation rules.
- Additionally, it explores minimax distinctions through a geometric lens and shares fundamental results on how to match algorithm performance effectively.
Article Abstract
We extend previous results concerning black box search algorithms, presenting new theoretical tools related to no free lunch (NFL) where functions are restricted to some benchmark (that need not be permutation closed), algorithms are restricted to some collection (that need not be permutation closed) or limited to some number of steps, or the performance measure is given. Minimax distinctions are considered from a geometric perspective, and basic results on performance matching are also presented.
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| http://dx.doi.org/10.1162/EVCO_a_00077 | DOI Listing |
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