Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Genetic algorithms

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    A heuristic approach for distributed generation sources location and capacity evaluation in distribution systems
    (2008) Sharma, K.M.; Vittal, K.; Seshagiri, P.
    Distributed Generation (DG) sources are becoming more prominent in distribution systems due to increased demand for the electrical energy. The locations and capacities of DG sources will have an impact on system losses, voltage profile characteristics of distribution network. This paper presents a heuristic approach for selection of optimal location and determination of optimal capacity of DG sources. The technique adopts Genetic Algorithms and Optimal Power Flow to facilitate the decision making process. The developed technique is incorporated with the flexibility so that the network planner can choose the total number of DGs to be included, their constraints on maximum power outputs, non-feasible locations for DG insertion which are to be excluded from search. The proposed approach is tested for IEEE 69 bus system and the results have indicated the versatility of the technique.
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    A phenomic approach to genetic algorithms for reconstruction of gene networks
    (2010) D'Souza, R.G.L.; Chandra Sekaran, K.C.; Kandasamy, A.
    Genetic algorithms require a fitness function to evaluate individuals in a population. The fitness function essentially captures the dependence of the phenotype on the genotype. In the Phenomic approach we represent the phenotype of each individual in a simulated environment where phenotypic interactions are enforced. In reconstruction type of problems, the model is reconstructed from the data that maps the input to the output. In the phenomic algorithm, we use this data to replace the fitness function. Thus we achieve survival-of-the- fittest without the need for a fitness function. Though limited to reconstruction type problems where such mapping data is available, this novel approach nonetheless overcomes the daunting task of providing the elusive fitness function, which has been a stumbling block so far to the widespread use of genetic algorithms. We present an algorithm called Integrated Pheneto-Genetic Algorithm (IPGA), wherein the genetic algorithm is used to process genotypic information and the phenomic algorithm is used to process phenotypic information, thereby providing a holistic approach which completes the evolutionary cycle. We apply this novel evolutionary algorithm to the problem of elucidation of gene networks from microarray data. The algorithm performs well and provides stable and accurate results when compared to some other existing algorithms. © 2010 Springer-Verlag Berlin Heidelberg.
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    Inference of Gene Networks from Microarray Data through a Phenomic Approach
    (2010) D'Souza, R.G.L.; Chandra Sekaran, K.C.; Kandasamy, A.
    The reconstruction of gene networks is crucial to the understanding of cellular processes which are studied in Systems Biology. The success of computational methods of drug discovery and disease diagnosis is dependent upon our understanding of the biological basis of the interaction networks between the genes. Better modelling of biological processes and powerful evolutionary methods are proving to be a key factor in the solution of such problems. However, most of these methods are based on processing of genotypic information. We present an evolutionary algorithm for inferring gene networks from expression data using phenotypic interactions. The benefit of this is that we avoid the need for an explicit objective function in the optimization process. In order to realize this, we have implemented a method called as the Phenomic algorithm and validated it for stability and accuracy in the reconstruction of gene networks. © Springer-Verlag Berlin Heidelberg 2010.
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    An optimal fuzzy logic controller tuned with artificial immune system
    (Springer Verlag service@springer.de, 2013) Omkar, S.N.; Ramaswamy, N.; Ananda, R.; Venkatesh, N.G.; Senthilnath, J.
    In this paper, a method for the tuning the membership functions of a Mamdani type Fuzzy Logic Controller (FLC) using the Clonal Selection Algorithm(CSA) a model of the Artificial Immune System(AIS) paradigm is examined. FLC's are designed for two problems, firstly the linear cart centering problem and secondly the highly nonlinear inverted pendulum problem. The FLC tuned by AIS is compared with FLC tuned by GA. In order to check the robustness of the designed FLC's white noise was added to the system, further, the masses of the cart and the length and mass of the pendulum are changed. The FLC's were also tested in the presence of faulty rules. Finally, Kruskal-Wallis test was performed to compare the performance of the GA and AIS. An insight into the algorithms are also given by studying the effect of the important parameters of GA and AIS. © 2013 Springer.