Garnett Carl Wilson

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We consider three open source codes that have the potential to provide the basis for research into evolutionary program code generation approaches to machine learning. Earlier reviews of information and tools for genetic programming, have considered online resources for ‘‘getting started’’ [1] and a commercial implementation of genetic programming(More)
of a consumer video game console (Xbox 360) for general programming on graphics processing unit (GPGPU) purposes. In particular, we implement a linear GP (LGP) system to solve classification and regression problems. We conduct interand intra-platform benchmarking of the Xbox 360 and PC, using GPU and CPU implementations on both architectures. Platform(More)
Developmental Genetic Programming (DGP) algorithms have explicitly required the search space for a problem to be divided into genotypes and corresponding phenotypes. The two search spaces are often connected with a genotype-phenotype mapping (GPM) intended to model the biological genetic code, where current implementations of this concept involve evolution(More)
A widely available and economic means of increasing the computing power applied to a problem is to use modern graphics processing units (GPUs) for parallel processing. We present a new, optimized general methodology for deploying genetic programming (GP) to the PC, Xbox 360 video game console, and Zune portable media device. This work describes, for the(More)
Developmental Genetic Programming (DGP) algorithms have been introduced where the search space for a problem is divided into genotypes and corresponding phenotypes that are connected by a mapping (or “genetic code”). Current implementations of this concept involve evolution of the mappings in addition to the traditional evolution of genotypes. We introduce(More)
A developmental co-evolutionary genetic programming approach (PAM DGP) is compared to a standard linear genetic programming (LGP) implementation for trading of stocks in the technology sector. Both interday and intraday data for these stocks were analyzed, where both implementations were found to be impressively robust to market fluctuations while reacting(More)
Foreign exchange (forex) market trading using evolutionary algorithms is an active and controversial area of research. We investigate the use of a linear genetic programming (LGP) system for automated forex trading of four major currency pairs. Fitness functions with varying degrees of conservatism through the incorporation of maximum drawdown are(More)
During the legal investigation of Enron Corporation, the U.S. Federal Regulatory Commission (FERC) made public a substantial data set of the company’s internal corporate emails. This work presents a genetic algorithm (GA) approach to social network analysis (SNA) using the Enron corpus. Three SNA metrics, degree, density, and proximity prestige, were(More)
Many data sets exist that contain both geospatial and temporal elements, in addition to the core data that requires analysis. Within such data sets, it can be difficult to determine how the data have changed over spatial and temporal ranges. In this design study we present a system for dynamically exploring geo-temporal changes in the data. GTdiff provides(More)