Publication | Closed Access
A Comparison of Maximum-Seeking Methods
89
Citations
8
References
1959
Year
Artificial IntelligenceEngineeringMachine LearningOptimal Experimental DesignOperations ResearchInformation RetrievalManagementIntelligent SearchingBiostatisticsCombinatorial OptimizationDecision TheoryStatisticsMaximum-seeking MethodsSequential Decision MakingComputer ScienceRandom MethodsExploration V ExploitationLocal Search (Optimization)Optimization ProblemSequential MethodsStatistical InferenceSearch TechniqueIterated Local SearchDecision ScienceSteepest Ascent
The steepest ascent, univariate, factorial, and random methods for seeking maxima were compared by trying versions of them in several two-factor situations, with and without experimental error. Of the two sequential methods, the steepest ascent was better than the univariate. Of the nonsequential methods, the factorial was better than the random. The sequential methods were superior to the nonsequential.
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