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The analysis of gene regulatory networks provides enormous information on various fundamental cellular processes involving growth, development, hormone secretion, and cellular communication. Their extraction from available gene expression profiles is a challenging problem. Such reverse engineering of genetic networks offers insight into cellular activity toward prediction of adverse effects of new...
Motivated by the growing demand of accuracy and low computational time in optimizing functions in various fields of engineering, an approach has been presented using the technique of parallel computing. The parallelization has been carried out on one of the simplest and flexible optimization algorithms, namely the particle swarm optimization (PSO) algorithm. PSO is a stochastic population global optimizer...
The authors have recently introduced a new generalised derivatives operator ????1,??2n,m, which generalised many well-known operators studied earlier by many different authors. The trend of finding new operators such as the differential or the integral operators has attracted many researchers and in fact, has become necessary to some of the researchers. The aim of this paper is to use the equality...
Service process usually needs to be tailored for reuse because of change of context. In this paper, we present a mining approach to mining process change sequences based on different context using partial derivatives of the factors affecting the change context, thus finding the best feasible sequence.
Differential Evolution (DE) is generally considered as a reliable, accurate and robust optimization technique. However, the algorithm suffers from slow convergence rate and takes large computational time for optimizing the computationally expensive objective functions. Therefore, an attempt to speed up DE is considered necessary. This research introduces a modified differential evolution, called Ant...
Most optimization problems have constraints of different types (e.g., physical, time, geometric, etc.), which modify the shape of the search space. We propose an ecologically inspired invasive weed optimization (IWO) algorithm to solve the constrained real-parameter optimization problems. Central to our approach is a parameter-free penalty function that we introduce. The adaptive nature of the penalty...
We study classical families of chemostat models with an arbitrary number of species competing for a single limiting substrate. For families of models, we present some obstructions to the existence of asymptotically stabilizable periodic trajectories. For other families of growth rates, we design a dilution rate and input substrate time-varying feedback controllers so that a positive trajectory of...
In this paper we have used a real coded genetic algorithm for finding the global minimum energy conformation of two small molecules viz. Pseudoethane and 1,2,3-trichloro-l-fluoro-propane based on a potential function. Finding the global minimum of this function is very difficult because it has a large number of local minima, which grows exponentially with molecule size. Computational results are obtained...
Invasive weed optimization (IWO) has been found to be a simple but powerful algorithm for function optimization over continuous spaces. It has reportedly outperformed many types of evolutionary algorithms and other search heuristics when tested over both benchmark and real-world problems. However the performance of most search heuristics deteriorates severely when applied to the task of optimization...
This paper presents an empirical analysis of the performance of differential evolution (DE) variants on different classes of unconstrained global optimization benchmark problems. This analysis has been undertaken to identify competitive DE variants which perform reasonably well on a range of problems with different features. Towards this, fourteen DE variants were implemented and tested on 14 high...
In this paper we present an empirical, comparative performance, analysis of fourteen variants of Differential Evolution (DE) and Dynamic Differential Evolution (DDE) algorithms to solve unconstrained global optimization problems. The aim is to compare DDE, which employs a dynamic evolution mechanism, against DE and to identify the competitive variants which perform reasonably well on problems with...
Differential evolution (DE) is a simple and efficient scheme for global optimization over continuous spaces. DE is generally considered as a reliable, accurate, robust and fast optimization techniques. It outperforms many other optimization algorithms in terms of convergence speed and robustness over common benchmark problems and real world applications. However, the user is required to set the values...
This paper presents a new diversity guided particle swarm optimization algorithm (PSO) named beta mutation PSO or BMPSO for solving global optimization problems. The BMPSO algorithm makes use of an evolutionary programming based mutation operator to maintain the level of diversity in the swarm population, thereby maintaining a good balance between the exploration and exploitation phenomena and preventing...
This paper investigates the effect of initiating the population with various probability distributions and low discrepancy sequences on the behavior of Particle Swarm Optimization (PSO). The probability distributions: Gaussian, Exponential, Beta and Gamma distribution and the low discrepancy sequences: Van der Corput and Sobol are considered in this study. Based on these probability distributions,...
In this paper, we study positive periodic solutions to the repulsive singular perturbation Hill equations with impulse effects. It is proved that such a perturbation problem has at least two positive impulsive periodic solutions when the anti-maximum principle holds for the Hill operator and the perturbation is superlinear at infinity. The proof relies on a nonlinear alternative of Leray-Schauder...
The assessment of co-expressed genes is an initial step to investigate the gene regulatory system in a microarray analysis. We designed a simple method to assess co-expressed genes from the expression profiles, based on the path consistency (PC) algorithm, which systematically infers a causal graph. The PC algorithm is simply modified for the frequent occurrence of high correlations between expression...
Biogeography-based optimization (BBO) is an evolutionary algorithm that is based on the science of biogeography. Biogeography is the study of the geographical distribution of organisms. In BBO, problem solutions are represented as islands, and the sharing of features between solutions is represented as migration between islands. This paper develops a Markov analysis of BBO, including the option of...
Complex data can often be represented in terms of random graphs or networks. Important features of real world networks can be described by a special class of random graphs called small-world networks. Small-world networks emerge in many contexts, from systems biology to distributed technological systems. Here we ask how the functional and structural properties of specialized real world networks are...
Nowadays, the management of sequential patterns data becomes an increasing need in biological knowledge discovery processes. An important task in these processes is the restitution of the results obtained by using data mining methods. In a complex domain as biomedical, an efficient interpretation of the patterns without any assistance is difficult. One of the most common knowledge discovery process...
Simulated semi-aerobic landfill equipment and anaerobic landfill equipment (CK) were installed, and the in-situ trace experiment based on the 15N isotope technique was carried out. The two equipments were respectively added with 4 g standard 15NH4Cl tracer(the abundance of 15N is 99.6%), and leachate samples at the bottom of the equipments were taken at 20 h, 44 h, 68 h, 116 h and 188 h after the...
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