多响应试验参数优化设计的Forest-Genetic法

IF 3.4 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence Pub Date : 2020-08-27 DOI:10.4114/INTARTIF.VOL23ISS66PP9-25

Adriana Villa-Murillo, A. Carrión, A. Sozzi

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引用次数: 0

摘要

我们提出了一种改进参数设计的方法，该方法由随机森林(RF)与遗传算法(GA)的结合组成，分为规范化、建模和优化三个阶段。第一个阶段对应于使用归一化函数对数据集的先前准备。在第二阶段，我们设计了一个适应多种质量特征的建模方案，我们称之为多元随机森林(MRF)来确定目标函数。最后，在第三阶段，我们在建立相应的遗传算法时，结合建模方案的性质和期望函数，得到了参数层次的最优组合。两个说明案例使我们能够比较和验证我们的方法与其他涉及人工神经网络(ANN)和模拟退火(SA)的建议的优点。

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Forest-Genetic method to optimize parameter design of multiresponse experiment

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).

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来源期刊

Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

2.00

自引率

0.00%

发文量

审稿时长

8 weeks

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.