MRI magnet design: search space analysis, EDAs and a real-world problem with significant dependencies
Pages 2141 - 2148
Abstract
This paper introduces the design of superconductive magnet configurations in Magnetic Resonance Imaging (MRI) systems as a challenging real-world problem for Evolutionary Algorithms (EAs). Analysis of the problem structure is conducted using a general statistical method, which could be easily applied to other problems. The results suggest that the problem is highly multimodal and likely to present a significant challenge for many algorithms. Through a series of preliminary experiments, a continuous Estimation of Distribution Algorithm (EDA) is shown to be able to generate promising designs with a small computational effort. The importance of utilizing problem-specific knowledge and the ability of an algorithm to capture dependencies in solving complex real-world problems is also highlighted.
References
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Caruana, R. and Mullin, M. Estimating the Number of Local Minima in Big, Nasty Search Spaces. In Proceedings of IJCAI-99 Workshop on Statistical Machine Learning for Large-Scale Optimization, 1999.
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Crozier, S. and Doddrell, D.M. Compact MRI Magnet Design by Stochastic Optimization. Journal of Magnetic Resonance, 127, 2 (1997), 233--237.
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Index Terms
- MRI magnet design: search space analysis, EDAs and a real-world problem with significant dependencies
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June 2005
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Published: 25 June 2005
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