Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Kolmogorov's theorem is relevant
Neural Computation
EvoBIO '09 Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
EvoBIO '09 Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
Generating linkage disequilibrium patterns in data simulations using genomeSIMLA
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Grammatical evolution of neural networks for discovering epistasis among quantitative trait loci
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
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Understanding the genetic underpinnings of common heritable human traits has enormous public health benefits with implications for risk prediction, development of novel drugs, and personalized medicine. Many complex human traits are highly heritable, yet little of the variability in such traits can be accounted for by examining single DNA variants at a time. Seldom explored non-additive gene-gene interactions are thought to be one source of this "missing" heritability. Approaches that can account for this complexity are more aptly suited to find combinations of genetic and environmental exposures that can lead to disease. Stochastic methods employing evolutionary algorithms have demonstrated promise in being able to detect and model gene-gene interactions that influence human traits, yet the search space is nearly infinite because of the vast number of variables collected in contemporary human genetics studies. In this work we assess the performance and feasibility of sensible initialization of an evolutionary algorithm using domain knowledge.