Evolving cellular automata to perform computations: mechanisms and impediments
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
Elements of machine learning
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Understanding the Crucial Role of AttributeInteraction in Data Mining
Artificial Intelligence Review
Evolution of Parallel Cellular Machines: The Cellular Programming Approach
Evolution of Parallel Cellular Machines: The Cellular Programming Approach
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Symbolic Discriminant Analysis for Mining Gene Expression Patterns
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
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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 gene-gene 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.