This paper proposes a novel face verification method using principal components analysis (PCA) and evolutionary algorithm (EA). Although PCA related algorithms have shown outstanding performance, the problem lies in m...
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This paper proposes a novel face verification method using principal components analysis (PCA) and evolutionary algorithm (EA). Although PCA related algorithms have shown outstanding performance, the problem lies in making decision rules or distance measures. To solve this problem, quantum-inspired evolutionary algorithm (QEA) is employed to find out the optimal weight factors in the distance measure for a predetermined threshold value which distinguishes between face images and non-face images. Experimental results show the effectiveness of the proposed method through the improved verification rate and false alarm rate. (C) 2004 Elsevier B.V. All rights reserved.
Many-objective optimization is very important for numerous practical applications. It, however, poses a great challenge to the Pareto dominance based evolutionary algorithms. In this paper, a fuzzy dominance based evo...
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Many-objective optimization is very important for numerous practical applications. It, however, poses a great challenge to the Pareto dominance based evolutionary algorithms. In this paper, a fuzzy dominance based evolutionary algorithm is proposed for many-objective optimization. The essence of the proposed algorithm is that it adaptively determines a fuzzy membership function for each objective of a given many-objective optimization problem and employs preferred reference points for clustering evolved solutions. Our algorithm uses distribution information of the evolving population to find preferred reference points from a set of generated reference points. The aim of using such preferred points is to emphasize both convergence and diversity of all the evolved solutions by maintaining cluster uniformity and handling irregular Pareto front. Extensive experimentation has been performed on a number of benchmark problems in evolutionary computing, including nine Waking-Fish-Group and seven Deb-Thiele-Laumanns-Zitzler benchmark problems having 2 to 25 objectives. In addition, we have investigated the performance of the proposed algorithm on three instances of degenerate Rectangle Problems. The experimental results show that the proposed algorithm is able to solve many-objective optimization problems efficiently, and it is compared favorably with the other evolutionary algorithms devised for such problems. A parametric study is also provided to understand the influence of a key parameter of the proposed algorithm.
The efficiency of material handling system requires an automation on the different levels of control and supervision to keep availability of the material handling devices for fast, safety and precise transferring mate...
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The efficiency of material handling system requires an automation on the different levels of control and supervision to keep availability of the material handling devices for fast, safety and precise transferring materials, as well as to reduce the maintenance cost, which is involved by enhancing the productivity of manufacturing process. In this paper, evolutionary-based algorithm for fuzzy logic-based data-driven predictive model of time between failures (TBF) and adaptive crane control system design is proposed. The heuristic searching strategy combining the arithmetical crossover, uniform and non-uniform mutation and deletion/insertion mutation is developed for optimizing the rules base (RB) and tuning the triangular-shaped membership functions to increase the efficiency and accuracy of a fuzzy rule-based system (FRBS). The evolutionary algorithm (EA) was employed to design a fuzzy predictive model based on the historical data of operational states monitored between the failures of the laboratory scaled overhead traveling crane electronic equipment. The fuzzy predictive model of TBF was implemented in the supervisory system created for supporting decision-making process through forecasting upcoming failure and delivering the user-defined maintenance strategies. The effectiveness of EA was also verified through designing a Takagi-Sugeno-Kang (TSK) fuzzy controller in the anti-sway crane control system. (C) 2013 Elsevier Ltd. All rights reserved.
The genetic robot has many configurable genes that contribute to defining the robot's personality. The large number of genes allows for a highly complex system, however it becomes increasingly difficult and time-c...
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The genetic robot has many configurable genes that contribute to defining the robot's personality. The large number of genes allows for a highly complex system, however it becomes increasingly difficult and time-consuming to ensure reliability, variability and consistency for the robot's personality while manually initializing values for the individual genes. To overcome this difficulty, this paper proposes MBTI-EAGRP. It is a fully autonomic gene-generative algorithm for a genetic robot's personality in a mobile phone. After grasping the user preferences through MBTI assessment using the neural network algorithm, the evolutionary algorithm generates and evolves a gene pool that customizes the robot's genome so that it closely matches a simplified set of personality features preferred by the user. Finally, an evaluation procedure for individuals is carried out in a virtual environment using tailored perception scenarios and real MBTI measurements. (C) 2012 Elsevier B.V. All rights reserved.
The space of ordered fuzzy numbers (OFN) that make possible to deal with fuzzy inputs quantitatively, exactly in the same way as with real numbers, is shortly presented. For defuzzyfication operators which play the ma...
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The space of ordered fuzzy numbers (OFN) that make possible to deal with fuzzy inputs quantitatively, exactly in the same way as with real numbers, is shortly presented. For defuzzyfication operators which play the main role in dealing with fuzzy controllers and fuzzy inference systems, an approximation formula is given and then a dedicated evolutionary algorithm is presented. (c) 2007 Elsevier Ltd. All rights reserved.
NPR may be seen as any attempt to create images to convey a scene without directly rendering a physical simulation. This paper describes an evolutionary algorithm (EA) for learning painting styles from example images....
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NPR may be seen as any attempt to create images to convey a scene without directly rendering a physical simulation. This paper describes an evolutionary algorithm (EA) for learning painting styles from example images. The evolutionary algorithm has two main stages: learning and painting. By learning the painting style from a source pair of training images (a source photograph and its artistic style), a style approximation can then be accomplished to another target image. The mechanism of evolutionary algorithms is used here to learn or capture different characters used to depict different regions of the source painting and use them to convert similar regions with similar texture statistics of the target image into artistic rendering. Two main perturbation operators are used in the proposed EA: a multi-sexual recombination and mutation operators. On overall, the algorithm, with its implementation simplicity and computation efficiency, can provide acceptable results with perceived artistic rendering.
Evapotranspiration of important indicator for management and planning of water resources. It is essential to analyze the evapotranspiration in order to improve water resources planning. The main goal of the study was ...
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Evapotranspiration of important indicator for management and planning of water resources. It is essential to analyze the evapotranspiration in order to improve water resources planning. The main goal of the study was to analyze the evapotranspiration based on several input parameters. It is important to estimate the influence of the input parameters on the evapotranspiration. For such a purpose evolutionary algorithm was applied. The algorithm applied in this article has space solution of genetic programs. Therefore this methodology is known as genetic programming. The input parameters in the model are monthly minimum and maximum air temperatures, sunshine hours, actual vapour pressure, minimum and maximum relative humidity and wind speed. Results presented in this study could be used for practical application of water resources planning and management based on the input parameters influence on the evapotranspiration.
Inherent multipath diffuse channel characteristics inhibit visible light communications (VLC) system to provide uniform and satisfactory communication performance within the room domain. An evolutionary algorithm base...
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Inherent multipath diffuse channel characteristics inhibit visible light communications (VLC) system to provide uniform and satisfactory communication performance within the room domain. An evolutionary algorithm based optimization scheme is proposed to modify the optical intensity of LED transmitters for reducing the signal power fluctuation extent. Simulation results show that the proposed scheme can efficiently reduce the signal power dynamic range up to 26.5% while the SNR is still sufficient for keeping BER< 10(-6) (using OOK-NRZ modulation format).
With the aim of improving energy conversion efficiency of dye-sensitised solar cells (DSCs), three evolutionary algorithms (EAs), namely genetic algorithm, particle swarm optimisation (PSO) and differential evolution,...
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With the aim of improving energy conversion efficiency of dye-sensitised solar cells (DSCs), three evolutionary algorithms (EAs), namely genetic algorithm, particle swarm optimisation (PSO) and differential evolution, are investigated the first time to extract the DSCs parameters based on the single-diode photovoltaic (PV) equivalent circuit model. By comparing the accuracy, calculation speed and anti-noise ability of the three EA techniques, PSO shows the highest accuracy and the best anti-noise property. To evaluate the parameters, especially the series-internal resistance (R-s) that is important for DSCs energy conversion efficiency, a batch of DSCs devices were made and the R-s obtained by changing the series resistance value connected with the DSCs. The two methods give the R-s approximately equal value, and almost same current-voltage figures based on PSO simulation with measured characteristics, which prove PSO is an efficient computational method and can be used to extract the parameters for the DSCs PV model.
This paper presents a non-traditional approach to compressive sensing, by developing an evolutionary algorithm-based method for signal reconstruction. Previous work in signal reconstruction in compressive sensing focu...
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This paper presents a non-traditional approach to compressive sensing, by developing an evolutionary algorithm-based method for signal reconstruction. Previous work in signal reconstruction in compressive sensing focused primarily on framing the problem as a convex optimisation that minimises the l1 norm of the signal. Minimising the l2 norm does not help as it leads to a non-sparse solution. Minimising the l0 norm is known to be NP-complete, thereby requiring exhaustive enumeration which is computationally prohibitive. Our approach is different from the methods adopted in the literature and is capable of handling l0-, l1- or l2-norm minimisation, and linear or nonlinear combinations thereof, in the same framework, yielding fairly good signal recovery with high probability. We provide empirical results on a number of test problems.
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