Current Issue : July - September Volume : 2016 Issue Number : 3 Articles : 5 Articles
Hybrid computational intelligence is defined as a combination of multiple intelligent algorithms such that the resulting model\nhas superior performance to the individual algorithms. Therefore, the importance of fusing two or more intelligent algorithms\nto achieve better performance cannot be overemphasized. In this work, a novel homogenous hybridization scheme is proposed\nfor the improvement of the generalization and predictive ability of support vector machines regression (SVR). The proposed and\ndeveloped hybrid SVR (HSVR) works by considering the initial SVR prediction as a feature extraction process and then employs\nthe SVR output, which is the extracted feature, as its sole descriptor. The developed hybrid model is applied to the prediction of\nreservoir permeability and the predicted permeability is compared to core permeability which is regarded as standard in petroleum\nindustry.The results show that the proposed hybrid scheme (HSVR) performed better than the existing SVR in both generalization\nand prediction ability.The outcome of this research will assist petroleum engineers to effectively predict permeability of carbonate\nreservoirs with higher degree of accuracy and will invariably lead to better reservoir. Furthermore, the encouraging performance\nof this hybrid will serve as impetus for further exploring homogenous hybrid system....
group of immune systems is similar to a multipopulation\nsystem. Immune systems can be influenced by\nvaccines and serums, similarly to that which occurs in nature.\nThe discussed algorithm has more parameters of work control\nthan other immune algorithms. Fractal and multi-fractal\nanalyses of the proposed algorithm, supported by quantitative\nanalysis, are discussed. Fractal and multi fractal analyses\nillustrate the algorithm behaviour. These analyses allow comparing\nalgorithm settings considering their impact on the\nexploration and exploitation of the solution space. Fractal\nand multi fractal analyses will be a valuable completion of\nknowledge of their work mechanisms....
Self-adaptive mechanisms for the identification\nof the most suitable variation operator in evolutionary algorithms\nrely almost exclusively on the measurement of the\nfitness of the offspring, which may not be sufficient to assess\nthe optimality of an operator (e.g., in a landscape with an high\ndegree of neutrality). This paper proposes a novel adaptive\noperator selection mechanism which uses a set of four fitness\nlandscape analysis techniques and an online learning algorithm,\ndynamic weighted majority, to provide more detailed\ninformation about the search space to better determine the\nmost suitable crossover operator. Experimental analysis on\nthe capacitated arc routing problem has demonstrated that\ndifferent crossover operators behave differently during the\nsearch process, and selecting the proper one adaptively can\nlead to more promising results....
Image re-ranking is a very effective technique to improve the results of web based image search. Most of the current commercial search engines (Bing, Google) are adopted this technique. In this paper new technique is proposed to improve the results of web based image search. In this technique, at the offline stage, different semantic spaces for a different query keyword are learned automatically. After that semantic signatures are obtained by projecting visual features of the images in to their related semantic spaces and hashing technique is used to compact obtained semantic signatures. At the online stage, compacted semantic signatures of the images are compared to re-rank images. This new technique improves accuracy and precision of web image search....
Least squares twin support vector machine\n(LSTSVM) is a relatively new version of support vector\nmachine (SVM) based on non-parallel twin hyperplanes.\nAlthough, LSTSVM is an extremely efficient and fast algorithm\nfor binary classification, its parameters depend on\nthe nature of the problem. Problem dependent parameters\nmake the process of tuning the algorithm with best values\nfor parameters very difficult, which affects the accuracy of\nthe algorithm. Simulated annealing (SA) is a random search\ntechnique proposed to find the global minimum of a cost\nfunction. It works by emulating the process where a metal\nslowly cooled so that its structure finally ââ?¬Å?freezesââ?¬Â. This\nfreezing point happens at a minimum energy configuration.\nThe goal of this paper is to improve the accuracy of the\nLSTSVM algorithm by hybridizing it with simulated annealing.\nOur research to date suggests that this improvement on\nthe LSTSVM is made for the first time in this paper. Experimental\nresults on several benchmark datasets demonstrate\nthat the accuracy of the proposed algorithm is very promising\nwhen compared to other classification methods in the\nliterature. In addition, computational time analysis of the\nalgorithm showed the practicality of the proposed algorithm\nwhere the computational time of the algorithm falls between\nLSTSVM and SVM...
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