Statistical machine learning for the cognitive selection of nonlinear programming algorithms in engineering design optimization

D. A. Hoeltzel, Wei-Hua Chieng

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

In order to overcome the problem of lack of generality in nonlinear programming (NLP) test problem formulation and to introduce the concept of cognitive NLP method switching, statistical machine learning has been applied to a sample data base of nonlinear programming problems. Reasonable conclusions have been drawn about an optimization problem type and a corresponding sequence of NLP solution algorithms using statistical pattern recognition applied to local (vs. global) design information. A program, referred to as OPTDEX-OLDM, with the capability of learning from statistical pattern recognition is discussed. The statistical aspects and algorithmic optimization of the nonlinear programming problem are emphasized in this discussion. A clustering process has been performed on attributes assigned to the NLP problem sample data base, and an example which describes this statistical clustering process is discussed.

原文English
主出版物標題Design Methods, Computer Graphics, and Expert
發行者American Society of Mechanical Engineers (ASME)
頁面67-74
頁數8
ISBN(列印)9780791897737
DOIs
出版狀態Published - 27 9月 1987
事件ASME 1987 Design Technology Conferences, DETC 1987 - Boston, United States
持續時間: 27 9月 198730 9月 1987

出版系列

名字Proceedings of the ASME Design Engineering Technical Conference
1

Conference

ConferenceASME 1987 Design Technology Conferences, DETC 1987
國家/地區United States
城市Boston
期間27/09/8730/09/87

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