Motion estimation (ME) is a process of correlating the frames by pointing the blocks in the candidate frame similar to those in the previous frame. Hexagonal Search (HS) reduces 10-15% search points over Diamond Searc...
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Motion estimation (ME) is a process of correlating the frames by pointing the blocks in the candidate frame similar to those in the previous frame. Hexagonal Search (HS) reduces 10-15% search points over Diamond Search (DS) with negligible Peak Signal to Noise Ratio (PSNR) loss. Adapting an optimization will achieve a massive improvement on the search process. The proposed HS-GA method combines the HS with geneticalgorithm (GA) to optimize ME. The HS-GA method reduces 72% on average search points pre block (ASPB) with PSNR loss of 2.5 dB and single generation. If the GA is executed with 10 generations the PSNR loss is reduced to 0.48 dB, but ASPB drastically increase to similar to 316%. The proposed work aims at multi objective on selection of better chromosomes and on control of generations to avoid the unnecessary generations to improve the GA. This process is named HSIGA(HS with Intelligent GA). The proposed HSIGA adapts five intelligent techniques like Histogram to decide new generations, Region based chromosome selection, Mutation on inner search gene, second rank valley point stopping criteria and interlaced HS to improve search process with negligible PSNR loss with in affordable search.
This paper presents a new geneticalgorithm approach to multiobjective optimization problems-incremental multiple objective geneticalgorithms (IMOGA). Different from conventional MOGA methods, it takes each objective...
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This paper presents a new geneticalgorithm approach to multiobjective optimization problems-incremental multiple objective geneticalgorithms (IMOGA). Different from conventional MOGA methods, it takes each objective into consideration incrementally. The whole evolution is divided into as many phases as the number of objectives, and one more objective is considered in each phase. Each phase is composed of two stages. First, an independent population is evolved to optimize one specific objective. Second, the better-performing individuals from the single-objecive population evolved in the above stage and the multiobjective population evolved in the last phase are joined together by the operation of integration. The resulting population then becomes an initial multiobjective population, to which a multiobjective evolution based on the incremented objective set is applied. The experiment results show that, in most problems, the performance of IMOGA is better than that of three other MOGAs, NSGA-II, SPEA, and PAES. IMOGA can find more solutions during the same time span, and the quality of solutions is better.
The study of the sensitivity and the specificity of a classification test constitute a powerful kind of analysis since it provides specialists with very detailed information useful for cancer diagnosis. In this work, ...
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The study of the sensitivity and the specificity of a classification test constitute a powerful kind of analysis since it provides specialists with very detailed information useful for cancer diagnosis. In this work, we propose the use of a multiobjective genetic algorithm for gene selection of Microarray datasets. This algorithm performs gene selection from the point of view of the sensitivity and the specificity. both used as quality indicators of the classification test applied to the previously selected genes. In this algorithm, the classification task is accomplished by Support Vector Machines: in addition a 10-Fold Cross-Validation is applied to the resulting subsets. The emerging behavior of all these techniques used together is noticeable, since this approach is able to offer, in an original and easy way. a wide range of accurate solutions to professionals in this area. The effectiveness of this approach is proved on public cancer datasets by working out new and promising results. A comparative analysis Of Our approach using two and three objectives, and with other existing algorithms. suggest that our proposal is highly appropriate for solving this problem. (C) 2009 Elsevier B.V. All rights reserved.
Low-impact development (LID) facilities constitute an important element of sponge cities. In this paper, a system of siting suitability indicators was established for green roofs, permeable pavement and rain gardens. ...
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Low-impact development (LID) facilities constitute an important element of sponge cities. In this paper, a system of siting suitability indicators was established for green roofs, permeable pavement and rain gardens. Based on the ArcGIS superposition analysis tool, the suitable area and area upper limit of LID facilities in each subcatchment were determined. Nonlinear functions of the LID deployment area and various rainfall characteristics were constructed using a multilayer perceptron (MLP) as a surrogate model. The results showed that (1) the upper limit of the suitable area for green roof deployment in each subcatchment ranged from 5.46% to 22.87%. The upper limit of the suitable area for permeable pavement application varied between 8.31% and 23.65%. The upper limit of the suitable area for rain garden deployment varied between 18.12% and 36.95%;(2) a final LID facility layout scheme was selected at a total outflow of 10,700 m3, peak discharge of 3.49 m3/s and total investment of 0.76 billion yuan during the construction and management period;(3) the total outflow reduction rate under the LID model ranged from 32.15% to 40.29%, and the peak discharge reduction rate ranged from 17.93% to 36.20%;(4) the computation time of the surrogate optimization model could be reduced to 0.37% of that of Storm Water Management Model (SWMM) conventional numerical optimisation. Herein, the surrogate model can be used to determine the optimal solution for the LID facility placement area assuming fixed reduction amounts of the outflow and peak discharge.
In order to simplify the offline identification of induction motor parameters, a method based on optimization using a multiobjective genetic algorithm is proposed. The non-dominated sorting geneticalgorithm (NSGA-II)...
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In order to simplify the offline identification of induction motor parameters, a method based on optimization using a multiobjective genetic algorithm is proposed. The non-dominated sorting geneticalgorithm (NSGA-II) is used to minimize the error between the actual data and an estimated model. The robustness of the method is shown by identifying parameters of the induction motor in three different cases. The simulation results show that the method successfully estimates the motor parameters.
This article proposes a novel approach that uses a mathematical model optimized by geneticalgorithms harmonized with the Russian theory of problem solving and invention (TRIZ) to design an export packing of Persian L...
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This article proposes a novel approach that uses a mathematical model optimized by geneticalgorithms harmonized with the Russian theory of problem solving and invention (TRIZ) to design an export packing of Persian Lime. The mathematical model (with functional elements of non-spatial type) optimizes the spaces of the Persian Lime Packing, maximizes the Resistance to Vertical Compression and minimizes the Amount of Material Used, according to the operation restrictions of the packing during the transport of the merchandise. This approach is developed in four phases: the identification of the solution space;the optimization of the conceptual design;the application of TRIZ;and the generation of the final proposal solution. The results show the proposed packing (with 28% less cardboard) supports at least the same vertical load with respect to the nearest competitor packing. However, with the same number of packings per pallet and pallets per container, the space used by the packing assembled and deployed in the container is greater by 10% and 38% respectively. Besides, TRIZ includes innovative non-spatial elements such as the airflow and the friction of the product inside the packing. The contribution of this approach can be replicable for the packing design of other horticultural products of the agri-food chain.
A comparative study between the conventional goal attainment strategy and an evolutionary approach using a geneticalgorithm has been conducted for the multiobjective optimization of the strength and ductility of low-...
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A comparative study between the conventional goal attainment strategy and an evolutionary approach using a geneticalgorithm has been conducted for the multiobjective optimization of the strength and ductility of low-carbon ferrite-pearlite steels. The optimization is based upon the composition and microstructural relations of the mechanical properties suggested earlier through regression analyses. After finding that a geneticalgorithm is more suitable for such a problem, Pareto fronts have been developed which give a range of strength and ductility useful in alloy design. An effort has been made to optimize the strength ductility balance of thermomechanically-processed high-strength multiphase steels. The objective functions are developed from empirical relations using regression and neural network modeling, which have the capacity to correlate high number of compositional and process variables, and works better than the conventional regression analyses.
State-of-the-art speech representations provide acceptable recognition results under optimal conditions, though their performance in adverse conditions still needs to be improved. In this direction, many advances invo...
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State-of-the-art speech representations provide acceptable recognition results under optimal conditions, though their performance in adverse conditions still needs to be improved. In this direction, many advances involving wavelet processing have been reported, showing significant improvements in classification performance for different kinds of signals. However, for speech signals, the problem of finding a convenient wavelet-based representation is still an open challenge. This study proposes the use of a multi-objective geneticalgorithm for the optimisation of a wavelet-based representation of speech. The most relevant features are selected from a complete wavelet packet decomposition in order to maximise phoneme classification performance. Classification results for English phonemes, in different noise conditions, show significant improvements compared with well-known speech representations.
This paper describes the extension of our earlier multiobjective method for generating plausible pharmacophore hypotheses to incorporate partial matches. Diverse sets of molecules rarely adopt exactly the same binding...
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This paper describes the extension of our earlier multiobjective method for generating plausible pharmacophore hypotheses to incorporate partial matches. Diverse sets of molecules rarely adopt exactly the same binding mode, and so allowing the identification of partial matches allows our program to be applied to larger and more diverse datasets. The method explores the conformational space of a series of ligands simultaneously with their alignment using a multiobjective genetic algorithm (MOGA). The principles of Pareto ranking are used to evolve a diverse set of pharmacophore hypotheses that are optimised on conformational energy of the ligands, the goodness of the overlay and the volume of the overlay. A partial match is defined as a pharmacophoric feature that is present in at least two, but not all, of the ligands in the set. The number of ligands that map to a given pharmacophore point is taken into account when evaluating an overlay. The method is applied to a number of test cases extracted from the Protein Data Bank (PDB) where the true overlay is known.
In this paper we present a genotype representation method for improving the performance of genetic-algorithm-based optimal design and synthesis of microelectromechanical systems. The geneticalgorithm uses a hierarchi...
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In this paper we present a genotype representation method for improving the performance of genetic-algorithm-based optimal design and synthesis of microelectromechanical systems. The geneticalgorithm uses a hierarchical component-based genotype representation, which incorporates specific engineering knowledge into the design optimization process. Each microelectromechanical system component is represented by a gene with its own parameters defining its geometry and the way it can be modified from one generation to the next. The object-oriented genotype structures efficiently describe the hierarchical nature typical of engineering designs. They also encode knowledge-based constraints that prevent the geneticalgorithm from wasting time exploring inappropriate regions of the search space. The efficiency of the hierarchical component-based genotype representation is demonstrated with surface-micromachined resonator designs.
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