Development of a soft computing-based framework for engineering design optimisation with quantitative and qualitative search spaces

Oduguwa, Victor; Roy, Rajkumar; Farrugia, Didier

doi:10.1016/j.asoc.2005.06.003

Cited by 19 publications

(7 citation statements)

References 18 publications

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“…Hence, algorithmically, there are a wide array of methods to include in our computational arsenal, and the reader is directed toward some of the references cited at the end of this article as a guide to the computational approaches that are available (Figure 4) (25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38). In the following discussion, we provide some examples of how, by utilizing these soft computing methods, we can harness the multiple V's of Big Data for knowledge discovery by taking advantage of the tools of machine learning and inference to help build an unsupervised learning framework for knowledge discovery, even when the volume or data size is relatively small.…”

Section: Predictive Models And/or Classificationsmentioning

confidence: 99%

Materials Informatics: The Materials “Gene” and Big Data

Rajan

2015

Annu. Rev. Mater. Res.

256

150

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Materials informatics provides the foundations for a new paradigm of materials discovery. It shifts our emphasis from one of solely searching among large volumes of data that may be generated by experiment or computation to one of targeted materials discovery via high-throughput identification of the key factors (i.e., "genes") and via showing how these factors can be quantitatively integrated by statistical learning methods into design rules (i.e., "gene sequencing") governing targeted materials functionality. However, a critical challenge in discovering these materials genes is the difficulty in unraveling the complexity of the data associated with numerous factors including noise, uncertainty, and the complex diversity of data that one needs to consider (i.e., Big Data). In this article, we explore one aspect of materials informatics, namely how one can efficiently explore for new knowledge in regimes of structure-property space, especially when no reasonable selection pathways based on theory or clear trends in observations exist among an almost infinite set of possibilities. 153 Annu. Rev. Mater. Res. 2015.45:153-169. Downloaded from www.annualreviews.org Access provided by Yale University -Law Library on 07/04/15. For personal use only.

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Section: Predictive Models And/or Classificationsmentioning

confidence: 99%

Materials Informatics: The Materials “Gene” and Big Data

Rajan

2015

Annu. Rev. Mater. Res.

256

150

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“…proposed a hierarchical fair competition model [32] that can significantly improve the search efficiency of evolutionary algorithms. Oduguwa [33] put forward an intelligent design framework that integrates the exploration in the qualitative space and the search in the quantitative space. This framework can well incorporate human knowledge and judgment, and substantially improves the practicality of the design method.…”

Section: Design Automation Of Mechatronic Systems (Or Mechatronic Desmentioning

confidence: 99%

Evolutionary synthesis of dynamical systems

Fan

Cai

et al. 2014

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

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When Electronic Design Automation (EDA) has achieved great success both in academy and industry, design automation for mechanical systems seems to be lagged behind. One underlying reason for this is that the coupling of subsystems of mechanical systems is very strong, whereas this coupling for digital electronic system is usually much weaker. Or in other words, the modularity of electronic systems, especially digital electronic systems is much stronger. On the other hand, the mechatronic systems are becoming more and more modularized, which makes them more amenable to be designed automatically, just as digital electronic systems do. In this sense, Mechatronic Design Automation (MDA) is very likely to become the next wave after EDA both in academy and industry. In this paper, we give a survey of the topic of evolutionary synthesis of dynamical systems in general, and wish to shed some light on the future development of this subject.

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“…Fuzzy logic (FL) is a technique which uses linguistic terms to capture qualitative values as that used in spoken language (Oduguwa et al, 2007). Examples of implicit descriptions are observed data or experience of experts.…”

Section: Process Design Aspectsmentioning

confidence: 99%