Kargupta, H. \A striking property of genetic code-like transformations ", Technical Report EECS-99-004, Department of Electrical Engineering and Computer Science, Washington State University, (1999).  Home/Search   Document Details and Download   Summary   Related Articles   Check

....map to a single phenotype) to increase connectivity in the solution space, or eliminate the generation of invalid solutions (and the need for repair or specialized operators) Some examples of approaches to studying genotype to phenotype transformations include the following. Kargupta and Park [20, 21] examine transformations of a fitness function into Fourier space as a way distributing fitness evaluation and show that for a given class of functions, such evaluations may be performed in polynomial time. Redundant representations, which investigate mappings where only a subset of the encoded ....

H. Kargupta. A striking property of genetic code-like transformations. Complex Systems, 11, 2001.

....map to a single phenotype) to increase connectivity in the solution space, or eliminate the generation of invalid solutions (and the need for repair or specialized operators) Some examples of approaches to studying genotype to phenotype transformations include the following. Kargupta and Park [20, 21] examine transformations of a fitness function into Fourier space as a way distributing fitness evaluation and show that for a given class of functions, such evaluations may be performed in polynomial time. Redundant representations, which investigate mappings where only a subset of the encoded ....

H. Kargupta. A striking property of genetic code-like transformations. Complex Systems, 11, 2001.

....expression patterns. Complex Systems, 11 (1997) 1 1 3 A variety of formal models for capturing the interactions and functional dependencies in genetic networks have been proposed in the literature. These include: electrical circuits [12] Boolean networks [1, 2, 14, 3, 15] Fourier coecients [4], Bayesian Networks [25] differential equations [16] Petri nets [17, 18] and Weight matrices [19] Each of these approaches has its own strengths and limitations in terms of the following considerations: faithfulness or accuracy of the model relative to the biological phenomenon being modeled; ....

Kargupta, H. \A striking property of genetic code-like transformations ", Technical Report EECS-99-004, Department of Electrical Engineering and Computer Science, Washington State University, (1999).

....the justi cation may come from the way the gene expression process works. As noted earlier, this process involves several representation transformations. The translation (transformation of mRNA to Protein) is one among them. This transformation is controlled by the genetic code. Our recent work [36, 37] extended our approach by exploring the e ect of genetic code like representation transformations in the domain of binary strings. This paper shows that genetic codelike transformations introduce an interesting property in the representation of a genetic tness function. It points out that such ....

H. Kargupta. A striking property of genetic code-like transformations. School of EECS Technical Report EECS{99-004, Washington State University, Pullman, WA, 1999. http://www.eecs.wsu.edu/~hillol/gene expression.html.

....Code that transforms the mRNA sequence to the Protein sequence in a living organism. This code, is de ned by a small redundant table that assigns an amino acid for every three consecutive nucleic acids in the mRNA. This paper investigates the role of genetic code like transformations (GCTs) [25, 31] in learning nonlinear functions from data. It considers the problem of learning a function from a given training data set with unknown underlying distribution. Randomized GCTs o er interesting properties useful for constructing an approximately linear representation of a nonlinear target ....

....of a nonlinear target function. This paper revisits the foundation of randomized GCTs and explores the analytical properties of such transformations. It also o ers several experimental results to demonstrate the potential of this approach. Section 2 revisits randomized GCTs developed elsewhere [25, 31]. It reviews multidimensional discrete Fourier transform, a tool used throughout this paper to study randomized GCTs. It also o ers the biological motivation behind this research. Section 3, introduces the GCTs themselves and motivates the main problems tackled in this paper understanding ....

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H. Kargupta. A striking property of genetic code-like transformations. Complex Systems Journal, 13(1):1-32, 2001.

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Kargupta, H. \A striking property of genetic code-like transformations ", Technical Report EECS-99-004, Department of Electrical Engineering and Computer Science, Washington State University, (1999).

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