Praveen Koduru

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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)
Epidermal growth factor receptor (EGFR)vIII is the most common EGFR mutant found in glioblastoma (GBM). EGFRvIII does not bind ligand, is highly oncogenic and is usually coexpressed with EGFR wild type (EGFRwt). EGFRvIII activates Met, and Met contributes to EGFRvIII-mediated oncogenicity and resistance to treatment. Here, we report that addition of EGF(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)
Particle swarm optimization is a stochastic algorithm for optimizing continuous functions. It uses a population of particles that follow trajectories through the search space towards good optima. This paper proposes adding a local search component to PSO to improve its convergence speed. Two possible methods are discussed. The first adds a term containing(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 proposes a new approach for biologically inspired computing on the basis of Gene Regulatory Networks. These networks are models of genes and dynamic interactions that take place between them. The differential equation representations of such networks resemble neural networks as well as idiotypic networks in immune system. Although several(More)