Stephen M. Welch

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Like many species, the model plant Arabidopsis thaliana exhibits multiple different life histories in natural environments. We grew mutants impaired in different signaling pathways in field experiments across the species' native European range in order to dissect the mechanisms underlying this variation. Unexpectedly, mutational loss at loci implicated in(More)
The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise(More)
research on developing efficient methods for estimating these parameters (Irmak et al., 2000), including attempts Crop simulation models incorporate many physiological processes to relate them to specific plant genotypes (White and within sophisticated mathematical frameworks. However, the control Hoogenboom, 1996). A valuable result of these studies(More)
Groundwater provides a reliable tap to sustain agricultural production, yet persistent aquifer depletion threatens future sustainability. The High Plains Aquifer supplies 30% of the nation's irrigated groundwater, and the Kansas portion supports the congressional district with the highest market value for agriculture in the nation. We project groundwater(More)
This paper presents a hybrid algorithm based on Genetic Programming (GP) and Particle Swarm Optimisation (PSO) for the automated recovery of gene network structure. It uses gene expression time series data as well as phenotypic data pertaining to plant flowering time as its input data. The algorithm then attempts to discover simple structures to approximate(More)
Selection on quantitative trait loci (QTL) may vary among natural environments due to differences in the genetic architecture of traits, environment-specific allelic effects or changes in the direction and magnitude of selection on specific traits. To dissect the environmental differences in selection on life history QTL across climatic regions, we grew a(More)
Hybrid algorithms that combine genetic algorithms with the Nelder-Mead simplex algorithm have been effective in solving certain optimization problems. In this article, we apply a similar technique to estimate the parameters of a gene regulatory network for flowering time control in rice. The algorithm minimizes the difference between the model behavior and(More)
This paper describes genetic and hybrid approaches for multiobjective optimization using a numerical measure called fuzzy dominance. Fuzzy dominance is used when implementing tournament selection within the genetic algorithm (GA). In the hybrid version, it is also used to carry out a Nelder–Mead simplex-based local search. The proposed GA is shown to(More)
This paper describes a PSO-Nelder Mead Simplex hybrid multi-objective optimization algorithm based on a numerical metric called µ -fuzzy dominance. Within each iteration of this approach, in addition to the position and velocity update of each particle using PSO, the k-means algorithm is applied to divide the population into smaller sized clusters. The(More)
Maintaining an appropriate balance between exploration and exploitation is the key to faster convergence. This paper proposes a method to add an exploitative component to particle swarm optimization, a recently proposed biologically inspired metaphor. This is accomplished by applying the well-known Nelder Mead simplex algorithm to the population of(More)