Conference Papers

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

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Subject: search.filters.subject.Fitness function

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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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    Generating test data for path coverage based testing using genetic algorithms
    (Springer Verlag service@springer.de, 2014) Panda, M.; Mohapatra, D.P.
    In this paper, we have developed an approach to generate test data for path coverage based testing using genetic algorithm. We have used control flow graph and cyclomatic complexity of the example program to find out the number of feasible paths present in the program and compared it with the actual number of paths covered by genetic algorithm. We have used genetic algorithm for generating test data automatically. We have shown that our algorithm is giving cent percent coverage, successfully covering all feasible paths. In our approach, we have observed that genetic algorithm is much more effective in generating test data within less time period, giving better coverage. © Springer India 2014.
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    Academic Curriculum Load Balancing using GA
    (Institute of Electrical and Electronics Engineers Inc., 2019) Chakradhar, M.; Charan, M.S.; Sai, R.U.; Kunal, M.; Vishnu Srinivasa Murthy, Y.V.S.; Koolagudi, G.K.
    In the paper, we propose an algorithm using genetic alogithm to find out the optimal solution for the academic load balancing problem. The load balancing problem is to optimize the load of credits per semester in an academic curriculum. In the proposed method, we try to distribute the course load as evenly as possible so that the deviation from the mean credit load per each semester is as minimal as possible. The objective function is to distribute the credit load among all the semesters evenly such that the deviation from the mean credits per semester is minimal. The proposed approach explores the solution space using only mutation operators and does not operate using crossover as the solutions obtained using cross over does not create any newer and better solutions in the solution space.The algorithm is applied on three data sets and the results are compared with the solutions obtained using the existing approaches. The results obtained using the state of the art solution are either better than approaches or on par with the state of art optimal solutions. The solution set obtained using the proposed approach is well spread out through out all the periods and all the periods contain almost mean number of credits. © 2019 IEEE.