Metalmodeling techniques for evolutionary optimization of computationally expensive problems: promises and limitations

It is often the case in many problems in science and engineering that the analysis codes used are computationally very expensive. This can pose a serious impediment to the successful application of evolutionary optimization techniques. Metamodeling techniques present an enabling methodology for reducing the computational cost of such optimization problems. We present here a general framework for coupling metamodeling techniques with evolutionary algorithms to reduce the computational burden of solving this class of optimization problems. This framework aims to balance the concerns of optimization with that of design of experiments. Experiments on test problems and a practical engineering design problem serve to illustrate our arguments. The practical limitations of this approach are also outlined

Topographical mapping assisted evolutionary search for multilevel optimization

In many problems in science and engineering, it is often the case that there exist a number of computational models to simulate the problem at hand. These models are usually trade-offs between accuracy and computational expense. Given a limited computation budget, there is need to develop a framework for selecting between different models in a sensible fashion during the search. The method proposed here is based on the construction of a heteroassociative mapping to estimate the differences between models, and using this information to guide the search. The proposed framework is tested on the problem of minimizing the transmitted vibration energy in a satellite boo

Metamodeling techniques for evolutionary optimization of computationally expensive problems: promises and limitations

It is often the case in many problems in science and engineering that the analysis codes used are computationally very expensive. This can pose a serious impediment to the successful application of evolutionary optimization techniques. Metamodeling techniques present an enabling methodology for reducing the computational cost of such optimization problems. We present here a general framework for coupling metamodeling techniques with evolutionary algorithms to reduce the computational burden of solving this class of optimization problems. This framework aims to balance the concerns of optimization with that of design of experiments. Experiments on test problems and a practical engineering design problem serve to illustrate our arguments. The practical limitations of this approach are also outlined.

Late recurrent adrenocortical carcinoma presenting radiologically as a gastrointestinal stromal tumour: A case report

Introduction: Adrenocortical carcinoma (ACC) is a rare malignancy with an estimated incidence of 1–2 per million people. It may recur, after complete surgical removal by local or distant metastasis. Observation: We report a case of late metastatic ACC presented as a mesenteric mass, 10 years post left adrenalectomy. Our case was initially misdiagnosed radiologically as gastrointestinal stromal tumour (GIST), and then the decision for exploration was made. The mass could be safely excised and confirmed pathologically to be an adrenocortical tumour

Late recurrent adrenocortical carcinoma presenting radiologically as a gastrointestinal stromal tumour: A case report

Introduction: Adrenocortical carcinoma (ACC) is a rare malignancy with an estimated incidence of 1–2 per million people. It may recur, after complete surgical removal by local or distant metastasis.Observation: We report a case of late metastatic ACC presented as a mesenteric mass, 10 years post left adrenalectomy. Our case was initially misdiagnosed radiologically as gastrointestinal stromal tumour (GIST), and then the decision for exploration was made. The mass could be safely excised and confirmed pathologically to be an adrenocortical tumour.Keywords: Adrenocortical carcinoma; GIST; Mesenter

Evolutionary Optimization with Dynamic Fidelity Computational Models

In this paper, we propose an evolutionary framework for model fidelity control that decides, at runtime, the appropriate fidelity level of the computational model, which is deemed to be computationally less expensive, to be used in place of the exact analysis code as the search progresses. Empirical study on an aerodynamic airfoil design problem based on a Memetic Algorithm with Dynamic Fidelity Model (MA-DFM) demonstrates that improved quality solution and efficiency are obtained over existing evolutionary schemes. © 2008 Springer-Verlag Berlin Heidelberg