Understanding the Crucial Role of AttributeInteraction in Data Mining
Artificial Intelligence Review
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
A new hybrid neural-genetic methodology for improving learning
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Evolving neural networks to identify bent-double galaxies in the FIRST survey
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
WeAidU-a decision support system for myocardial perfusion images using artificial neural networks
Artificial Intelligence in Medicine
Incorporating domain knowledge into evolutionary computing for discovering gene-gene interaction
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
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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Evolutionary algorithms such as genetic programming and grammatical evolution have been used for simultaneously optimizing network architecture, variable selection, and weights for artificial neural networks. Using an evolutionary algorithm to perform variable selection while searching for non-linear interactions is akin to searching for a needle in a haystack. There is, however, a considerable amount of correlation among variables in biological datasets, such as in microarray or genetic studies. Using the XOR problem, we show that correlation between non-functional and functional variables alters the variable selection fitness landscape by broadening the fitness peak over a wider range of potential input variables. Furthermore, when sub-optimal weights are used, local optima in the variable selection fitness landscape appear centered on each of the two functional variables. These attributes of the fitness landscape may supply building blocks for evolutionary search procedures, and may provide a rationale for conducting a local search for variable selection.