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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)
—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)
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)
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)
Integrated environmental modeling enables the development of comprehensive simulations by compositing individual models within and across disciplines. The Simple Script Wrapper (SSW), developed here, provides a foundation for model linkages and integrated studies. The Open Modeling Interface (OpenMI) enables model integration but it is challenging to(More)
This paper describes a PSO-Nelder Mead Simplex hybrid multi-objective optimization algorithm based on a numerical metric called ε-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)
— This paper describes a scalable approach to one of the most computationally intensive problems in molecular plant breeding, that of associating quantitative traits with genetic markers. The fundamental problem is to build statistical correlations between particular loci in the genome of an individual plant and the expressed characteristics of that(More)