Titlebook: Swarm, Evolutionary, and Memetic Computing; 6th International Co Bijaya Ketan Panigrahi,Ponnuthurai Nagaratnam Suga Conference proceedings

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Development of Back Propagation Neural Network (BPNN) Model to Predict Combustion Parameters of Dien layer is varied from 3 to 22, to architect a suitable network for the prediction of combustion parameters with good accuracy. Test results show that network with 4-19-4 architecture with trainlm algorithm can predict the four parameters (ID, CD, PP and HR) with correlation coefficients as 0.9892,

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Variance Based Particle Swarm Optimization for Function Optimization and Feature Selection,henceforth. In VPSO, the velocity is influenced by the variance of the population. When the variance of the population is high, particles make use of exploitation and vice versa. This reduces the effect of swamping in local optimum. We have validated VPSO method for function optimization and feature

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Analysis of Next-Generation Sequencing Data of miRNA for the Prediction of Breast Cancer, breast cancer. In this regard, we have developed a technique using Gravitation Search Algorithm, which optimizes the underlying classification performance of Support Vector Machine. The proposed technique is able to select the potential features, in this case miRNAs, in order to achieve better pred

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Multi-objective Power Dispatch Using Stochastic Fractal Search Algorithm and TOPSIS,nd 13 generating unit systems. Results of stochastic fractal search algorithm with weighted sum method (SFSA_WS) are compared with SFSA with TOPSIS (SFSA_TOP) method. The results obtained by both cases are also compared with those available in recent literature, which confirms the potential of SFSA_

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