Abstract
An important goal of human genetics is to identify variations in genes that are associated with risk of disease. This goal is complicated by the fact that, for common multifactorial diseases such as hypertension, interactions between genetic variations are likely to be more important than the independent effects of any single genetic variation. Attribute interaction is a well-known problem in data mining and is a complicating factor in genetic data analysis. We have previously addressed this problem by developing a parallel approach to problem solving that utilizes one-dimensional cellular automata (CA) for knowledge representation and genetic algorithms (GA) for optimization. In this study, we evaluate the power of this parallel CA approach by simulating genegene interactions and adding noise from several common real-world sources. These simulation studies document the strengths of the CA approach and document a weakness that needs to be addressed.
Keywords
- Genetic Algorithm
- Cellular Automaton
- Cellular Automaton
- Parallel Problem
- Multifactor Dimensionality Reduction
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Moore, J.H., Hahn, L.W. (2002). Cellular Automata and Genetic Algorithms for Parallel Problem Solving in Human Genetics. In: Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45712-7_79
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DOI: https://doi.org/10.1007/3-540-45712-7_79
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