首页|New Findings on Intelligent Systems Described by Investigators at University of New South Wales (Adjusting Normalization Bounds To Improve Hypervolume Based Search for Expensive Multi-objective Optimization)
New Findings on Intelligent Systems Described by Investigators at University of New South Wales (Adjusting Normalization Bounds To Improve Hypervolume Based Search for Expensive Multi-objective Optimization)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Fresh data on Machine Learning - Intelligent Systems are presented in a new report.According to news reporting from Canberra, Australia, by NewsRx journalists, research stated, “Whensolving expensive multi-objective optimization problems, surrogate models are often used to reduce thenumber of true evaluations. Based on predictions from the surrogate models, promising candidate solutions,also referred to as infill solutions, can be identified for evaluation to expedite the search towards theoptimum.”
CanberraAustraliaAustralia and New ZealandIntelligent SystemsMachine LearningUniversity of New South Wales
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