{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T06:20:45Z","timestamp":1649139645041},"reference-count":9,"publisher":"Cambridge University Press (CUP)","issue":"2","license":[{"start":{"date-parts":[[2009,2,27]],"date-time":"2009-02-27T00:00:00Z","timestamp":1235692800000},"content-version":"unspecified","delay-in-days":4715,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIEDAM"],"published-print":{"date-parts":[[1996,4]]},"abstract":"<jats:p>Most of machine learning in design has focussed on learning generalizations to predict some future behavior of the system under consideration. Such approaches have been applied primarily to the analysis and synthesis stages of designing. There has been little work done relating to the formulation stage. This paper applies a particular machine learning approach to the improvement of the formal description of the design formulation. It applies an evolutionary technique to the problem reformulation to improve the formulation. This results in both a near optimal problem formulation and an improvement in the solution synthesized from that formulation.<\/jats:p>","DOI":"10.1017\/s0890060400001414","type":"journal-article","created":{"date-parts":[[2010,3,31]],"date-time":"2010-03-31T13:45:57Z","timestamp":1270043157000},"page":"147-148","source":"Crossref","is-referenced-by-count":0,"title":["Improving design problem formulations using machine learning"],"prefix":"10.1017","volume":"10","author":[{"given":"John S.","family":"Gero","sequence":"first","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2009,2,27]]},"reference":[{"key":"S0890060400001414_ref001","doi-asserted-by":"publisher","DOI":"10.1016\/S0020-7373(87)80042-1"},{"key":"S0890060400001414_ref008","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4613-2279-5"},{"key":"S0890060400001414_ref009","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/64.223989","article-title":"A knowledge-based equation discovery system for engineering domains","volume":"8","author":"Rao","year":"1993","journal-title":"IEEE Expert"},{"key":"S0890060400001414_ref002","first-page":"1","volume-title":"Machine Learning Paradigms and Methods","author":"Carbonell","year":"1990"},{"key":"S0890060400001414_ref003","doi-asserted-by":"publisher","DOI":"10.1017\/S089006040000069X"},{"key":"S0890060400001414_ref004","volume-title":"Genetic algorithms in search, optimization and machine learning","author":"Goldberg","year":"1989"},{"key":"S0890060400001414_ref005","first-page":"1448","volume-title":"Computing in Civil and Building Engineering","author":"Gunaratnam","year":"1993"},{"key":"S0890060400001414_ref006","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8667.1992.tb00418.x"},{"key":"S0890060400001414_ref007","first-page":"1432","volume-title":"Computing in Civil and Building Engineering","author":"Maher","year":"1993"}],"container-title":["Artificial Intelligence for Engineering Design, Analysis and Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0890060400001414","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,12]],"date-time":"2019-05-12T21:53:43Z","timestamp":1557698023000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0890060400001414\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1996,4]]},"references-count":9,"journal-issue":{"issue":"2","published-print":{"date-parts":[[1996,4]]}},"alternative-id":["S0890060400001414"],"URL":"https:\/\/doi.org\/10.1017\/s0890060400001414","relation":{},"ISSN":["0890-0604","1469-1760"],"issn-type":[{"value":"0890-0604","type":"print"},{"value":"1469-1760","type":"electronic"}],"subject":[],"published":{"date-parts":[[1996,4]]}}}