Learn More
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)
—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 sim-plex-based local search. The proposed GA is shown to(More)
In this paper, the parameters of a genetic network for rice flowering time control have been estimated using a multi-objective genetic algorithm approach. We have modified the recently introduced concept of fuzzy dominance to hybridize the well-known Nelder Mead Simplex algorithm for better exploitation with a multi-objective genetic algorithm. A(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)
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 solutions at the end of each iteration. It(More)