Designing and Analyzing Computational Experiments for Global Optimization

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dc.contributor.authorTrosset, Michael W.en_US
dc.contributor.authorPadula, Anthony D.en_US
dc.date.accessioned2018-06-18T17:48:14Zen_US
dc.date.available2018-06-18T17:48:14Zen_US
dc.date.issued2000-07en_US
dc.date.noteJuly 2000en_US
dc.description.abstractWe consider a variety of issues that arise when designing and analyzing computational experiments for global optimization. We describe a probability model for objective functions and a method for generating pseudorandom objective functions. We argue in favor of evaluating the performance of global optimization algorithms by measuring the depth of the objective function achieved with a fixed number of function evaluations. We emphasize the importance of replication in computational experiments and describe some useful statistical techniques for assimilating results. We illustrate our methods by performing a small study that compares two multistart strategies for global optimization.en_US
dc.format.extent18 ppen_US
dc.identifier.citationTrosset, Michael W. and Padula, Anthony D.. "Designing and Analyzing Computational Experiments for Global Optimization." (2000) <a href="https://hdl.handle.net/1911/101953">https://hdl.handle.net/1911/101953</a>.en_US
dc.identifier.digitalTR00-25en_US
dc.identifier.urihttps://hdl.handle.net/1911/101953en_US
dc.language.isoengen_US
dc.titleDesigning and Analyzing Computational Experiments for Global Optimizationen_US
dc.typeTechnical reporten_US
dc.type.dcmiTexten_US
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