{"title":"Forest-Genetic method to optimize parameter design of multiresponse experiment","authors":"Adriana Villa-Murillo, A. Carrión, A. Sozzi","doi":"10.4114/INTARTIF.VOL23ISS66PP9-25","DOIUrl":null,"url":null,"abstract":"We propose a methodology for the improvement of the parameter design that consists of the combination ofRandom Forest (RF) with Genetic Algorithms (GA) in 3 phases: normalization, modelling and optimization.The rst phase corresponds to the previous preparation of the data set by using normalization functions. In thesecond phase, we designed a modelling scheme adjusted to multiple quality characteristics and we have called itMultivariate Random Forest (MRF) for the determination of the objective function. Finally, in the third phase,we obtained the optimal combination of parameter levels with the integration of properties of our modellingscheme and desirability functions in the establishment of the corresponding GA. Two illustrative cases allow us tocompare and validate the virtues of our methodology versus other proposals involving Arti cial Neural Networks(ANN) and Simulated Annealing (SA).","PeriodicalId":43470,"journal":{"name":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","volume":"49 1","pages":"9-25"},"PeriodicalIF":3.7000,"publicationDate":"2020-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4114/INTARTIF.VOL23ISS66PP9-25","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
We propose a methodology for the improvement of the parameter design that consists of the combination ofRandom Forest (RF) with Genetic Algorithms (GA) in 3 phases: normalization, modelling and optimization.The rst phase corresponds to the previous preparation of the data set by using normalization functions. In thesecond phase, we designed a modelling scheme adjusted to multiple quality characteristics and we have called itMultivariate Random Forest (MRF) for the determination of the objective function. Finally, in the third phase,we obtained the optimal combination of parameter levels with the integration of properties of our modellingscheme and desirability functions in the establishment of the corresponding GA. Two illustrative cases allow us tocompare and validate the virtues of our methodology versus other proposals involving Arti cial Neural Networks(ANN) and Simulated Annealing (SA).
期刊介绍：
Inteligencia Artificial is a quarterly journal promoted and sponsored by the Spanish Association for Artificial Intelligence. The journal publishes high-quality original research papers reporting theoretical or applied advances in all branches of Artificial Intelligence. The journal publishes high-quality original research papers reporting theoretical or applied advances in all branches of Artificial Intelligence. Particularly, the Journal welcomes: New approaches, techniques or methods to solve AI problems, which should include demonstrations of effectiveness oor improvement over existing methods. These demonstrations must be reproducible. Integration of different technologies or approaches to solve wide problems or belonging different areas. AI applications, which should describe in detail the problem or the scenario and the proposed solution, emphasizing its novelty and present a evaluation of the AI techniques that are applied. In addition to rapid publication and dissemination of unsolicited contributions, the journal is also committed to producing monographs, surveys or special issues on topics, methods or techniques of special relevance to the AI community. Inteligencia Artificial welcomes submissions written in English, Spaninsh or Portuguese. But at least, a title, summary and keywords in english should be included in each contribution.
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