Towards Autonomous Evolutionary Design Systems via Grid — Based Technologies

Parmee, Ian C.; Abraham, Joanna; Shackelford, M.; Rana, O. F.; ShaikhAli, A.

doi:10.1061/40794(179)118

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…Well known examples in this category are the proprietary tools developed by LMS/Noesis (Optimus, Virual.Lab) and Vanderplaats R&D (VisualDOC). From academia, the more prominent projects are Geodise [28] from the University of Southampton, and the DAKOTA toolkit from Sandia National Labs [29].…”

Section: Related Workmentioning

confidence: 99%

Automatic Approximation of Expensive Functions with Active Learning

Gorissen

Crombecq

Couckuyt

et al. 2009

Studies in Computational Intelligence

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Abstract. The use of computer simulations has become a viable alternative for real-life controlled experiments. Due to the computational cost of these high fidelity simulations, surrogate models are often employed as an approximation for the original simulator. Because of their compact formulation and negligible evaluation time, global surrogate models are very useful tools for exploring the design space, optimization, visualization and sensitivity analysis. Additionally, multiple surrogate models can be chained together to model large scale systems where the direct use of the original expensive simulators would be too cumbersome. Many surrogate model types, such as neural networks, support vector machines and rational models have been proposed, and many more techniques have been developed to minimize the number of expensive simulations required to train a sufficiently accurate surrogate model. In this chapter, we present a fully automated and integrated global surrogate modeling methodology for regression modeling and active learning that readily enables the adoption of advanced global surrogate modeling methods by application scientists. The work brings together insights from distributed systems, artificial intelligence, and modeling & simulation, and has applications in a very wide range of fields. The merits of this approach are illustrated with several examples, and several surrogate model types.

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Section: Related Workmentioning

confidence: 99%

Automatic Approximation of Expensive Functions with Active Learning

Gorissen

Crombecq

Couckuyt

et al. 2009

Studies in Computational Intelligence

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“…There will be services for evolutionary computations, data management of evolutionary results, middleware for handling interoperability among different objective functions for multi-objective optimisation processes, visualisation services for output, collaboration services where optimisation expertise are distributed across geographical locations and modeling and search services. [26] developed a service-oriented system for design optimisation using genetic algorithm (GA), simulated annealing (SA) algorithm and Tabu Search (TS) algorithm. The system has data and parametric model located in different sites of Evolutionary Computing within Grid Environment 9…”

Section: Service-oriented Architecturementioning

confidence: 99%

“…It can be seen that PSEs integrate services, computational resources and people both within and outside organisations through Enterprise and Extraprise Grids. PSEs support remote problem definition that leads to the selection and application of appropriate engineering design search, exploration and optimisation techniques using evolutionary algorithms such as GA and genetic programming (GP) for the optimisation of multidisciplinary engineering processes [26]. The need for workflow management within this crossdomain and dynamic service demand and consumption calls for fuzzy timing techniques as part of the scheduling algorithms in PSEs [7].…”

Section: Figure 4 a Hypothetical Car Manufacturer (Oem) With Many Supmentioning

confidence: 99%

Evolutionary Computing within Grid Environment

Tiwari

Goteng

Roy

2007

Advances in Evolutionary Computing for System Design

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Evolutionary computing (EC) techniques such as genetic algorithm (GA), genetic programming (GP), evolutionary programming (EP) and evolution strategies (ES) mimic nature through natural selection to perform complex optimisation processes that require more than one solutions. Grid-enabled environment provides suitable framework for EC techniques due to its computational and data capabilities. In addition, the semantic and knowledge Grids aid in the design search and exploration for multi-objective optimisation tasks. This chapter explores some problem solving environments such as Geodise (Grid-Enabled Optimisation Design Search for Engineering), FIPER (Federated Intelligent Product Environment), SOCER (Service-Oriented Concurrent Environment), DAME (Distributed Aircraft Maintenance Environment) and Globus toolkit to demonstrate how EC techniques can be performed more efficiently within Grid environment. Service-oriented and autonomic computing features of Grid are discussed to highlight how EC algorithms can be published as services by service providers and used by service requestors dynamically. Grid computational steering and visualisation are features that can be used for real-time tuning of parameters and visual display of optimal solutions. This chapter demonstrates that grid-enabled evolutionary computing marks the future of optimisation techniques. Intelligence, 66, Studies in Computational

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