Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Foundations of genetic programming
Foundations of genetic programming
Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming!
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Complex function sets improve symbolic discriminant analysis of microarray data
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Cross validation consistency for the assessment of genetic programming results in microarray studies
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Exploiting expert knowledge in genetic programming for genome-wide genetic analysis
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
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The study of common, complex multifactorial diseases in genetic epidemiology is complicated by nonlinearity in the genotype-to-phenotype mapping relationship that is due, in part, to epistasis or gene-gene interactions. Symobolic discriminant analysis (SDA) is a flexible modeling approach which uses genetic programming (GP) to evolve an optimal predictive model using a predefined collection of mathematical functions, constants, and attributes. This has been shown to be an effective strategy for modeling epistasis. In the present study, we introduce the genetic .mask. as a novel building block which exploits expert knowledge in the form of a pre-constructed relationship between two attributes. The goal of this study was to determine whether the availability of.mask.building blocks improves SDA performance. The results of this study support the idea that pre-processing data improves GP performance.