# A Fitness Function Elimination Theory For Blackbox Optimization And Problem Class Learning

@inproceedings{Anil2012AFF, title={A Fitness Function Elimination Theory For Blackbox Optimization And Problem Class Learning}, author={Gautham Anil}, year={2012} }

The modern view of optimization is that optimization algorithms are not designed in a vacuum, but can make use of information regarding the broad class of objective functions from which a problem instance is drawn. Using this knowledge, we want to design optimization algorithms that execute quickly (efficiency), solve the objective function with minimal samples (performance), and are applicable over a wide range of problems (abstraction). However, we present a new theory for blackbox…

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The design of generally well-performing black-box algorithms for expected-utility maximization problems is interested, that is, algorithms which have access to the utility function only via input–output queries.

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