The model of visual attention proposed by Stentiford can effectively identify salient regions of an image. Since Stentiford model is produced by using evolutionary programming (EP), which usually involves a lot of...
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The model of visual attention proposed by Stentiford can effectively identify salient regions of an image. Since Stentiford model is produced by using evolutionary programming (EP), which usually involves a lot of computation, a fast region of interest (ROI) extraction approach is proposed. The input image is firstly bilinear down-sampled. The visual attention map is calculated with occurrence frequency of a region in the image. The candidate ROI is obtained after Tilling holes and filtering isolated points. The actual ROI is extracted by scaling the candidate ROI with bilinear interpolation. Experimental results show that the proposed approach can faster extract ROI, and have a more accurate ROI detection as well.
This study focuses on the position feedback control problem of DC servo motors.A suitable IAE fitness function is selected,and then the methods of GA and EP are used to find the optimal PID control gain *** on these o...
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This study focuses on the position feedback control problem of DC servo motors.A suitable IAE fitness function is selected,and then the methods of GA and EP are used to find the optimal PID control gain *** on these optimal PID control gain constants,we add an FLC to the constants to make them the fine tuning gain *** result shows that a better control response can be obtained by combining the FLC with optimal PID ***,a DC servo motor produced by Quanser *** used to verify its effectiveness.
This paper presents the development and analysis of an efficient, evolutionary, intelligent, genetic modelling method for cutting forces in ball-end milling. An evolutionary method using genetic programming (GP) and e...
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ISBN:
(纸本)9783901509575
This paper presents the development and analysis of an efficient, evolutionary, intelligent, genetic modelling method for cutting forces in ball-end milling. An evolutionary method using genetic programming (GP) and evolutionary programming approaches is proposed. In the genetic model, all the influencing parameters influencing on the size of the cutting forces during the milling process are considered. The presented model can be used inflexible manufacturing for reduction of the total machining time, for the increase of accuracy, reliability, productivity and for decreasing the machining costs. The goal of the paper is the development of mathematical equation for the cutting force in ball-end milling process with genetic programming.
A cultural algorithm is analyzed and *** application of this algorithm for solving complex constrained optimization problems has also been discussed. In this approach,an improved evolutionary programming is used as a ...
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A cultural algorithm is analyzed and *** application of this algorithm for solving complex constrained optimization problems has also been discussed. In this approach,an improved evolutionary programming is used as a population space,in which a shift factor is proposed;according to the corresponding population space, the knowledge sources contained in the belief space of cultural algorithm are specifically designed and are used to guide the evolutionary search,The approach not only can maintain quite nicely the population diversity,but also can help to converge to the global optimal solution rapidly, which is validated by some experimental results.
A new mutation operator based on the T probability distribution is studied. The T probability distribution is stable and can generate an offspring that is farther away from its parent than the commonly employed Gaussi...
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ISBN:
(纸本)9781424404759
A new mutation operator based on the T probability distribution is studied. The T probability distribution is stable and can generate an offspring that is farther away from its parent than the commonly employed Gaussian mutation. Moreover, it has a better fine-tuning ability than the Cauchy mutation. In this paper, evolutionary programming (EP) with mutations based on the T probability distribution is studied. The new algorithm is tested on 23 benchmark functions and compared with the conventional EP and the fast EP. The experimental results demonstrate that the performance of the proposed algorithm outperforms the conventional EP and the fast EP.
In this paper, self-adaptive real coded genetic algorithm (SARGA) is used as one of the techniques to solve optimal reactive power dispatch (ORPD) problem. The self-adaptation in real coded genetic algorithm (RGA) is ...
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In this paper, self-adaptive real coded genetic algorithm (SARGA) is used as one of the techniques to solve optimal reactive power dispatch (ORPD) problem. The self-adaptation in real coded genetic algorithm (RGA) is introduced by applying the simulated binary crossover (SBX) operator. The binary tournament selection and polynomial mutation are also introduced in real coded genetic algorithm. The problem formulation involves continuous (generator voltages), discrete (transformer tap ratios) and binary (var Sources) decision variables. The stochastic based SARGA approach can handle all types of decision variables and produce near optimal solutions. The IEEE 14- and 30-bus systems were used as test systems to demonstrate the applicability and efficiency of the proposed method. The performance of the proposed method is compared with evolutionary programming (EP) and previous approaches reported in the literature. The results show that SARGA solves the ORPD problem efficiently. (C) 2008 Elsevier B.V. All rights reserved.
A evolutionary approach to solve the multiobjective optimization problems, mixed strategies Pareto evolutionary programming (MSPEP), is presented. Based on the performance of mutation strategies, the mixed strategy di...
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A evolutionary approach to solve the multiobjective optimization problems, mixed strategies Pareto evolutionary programming (MSPEP), is presented. Based on the performance of mutation strategies, the mixed strategy distribution is dynamically adjusted. By combining the Pareto strength ranking procedure with the mixed mutation strategies, a new evolutionary algorithm is proposed. The proposed approach is compared with other evolutionary optimization techniques in several benchmark functions. Experimental results demonstrate that the proposed method could rapidly converge to the Pareto optimal front and spread widely along the front.
This article presents the solution of optimal power flow (OPF) problem of generator units with ramp rate limits and non-smooth fuel functions. In this article evolutionary programming (EP) algorithm is devoted to solv...
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This article presents the solution of optimal power flow (OPF) problem of generator units with ramp rate limits and non-smooth fuel functions. In this article evolutionary programming (EP) algorithm is devoted to solve the OPF problem with non-smooth fuel cost functions like quadratic, piece-wise, valve point loading and combined cycle cogeneration plants. In the proposed EP algorithm, mutation is changing non-linearly with respect to the number of generations to avoid premature condition. The proposed EP algorithm is demonstrated to solve OPF problem for IEEE-30 bus system and Indian utility-62 bus system with line flow constraints. The line flows in MVA are computed directly from Newton Raphson method The test results prove that the EP method is simpler and more efficient for solving OPF problem with non-smooth fuel cost functions with many constraints.
This paper presents a novel approach to multi-area economic dispatch problems with multiple fuel options using a hybrid evolutionary programming method. The objective is to minimize the operation cost of the entire sy...
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This paper presents a novel approach to multi-area economic dispatch problems with multiple fuel options using a hybrid evolutionary programming method. The objective is to minimize the operation cost of the entire system while satisfying the tie line constraints. In this paper, EP-LMO (evolutionary programming with Levenberg-Marquardt Optimization) technique is proposed to solve multi-area economic dispatch problems with multiple fuel options. The EP-LMO is developed in such a way that a simple evolutionary programming (EP) is applied as a base level search to find the direction of the optimal global region. And Levenberg-Marquardt Optimization (LMO) method is used as a fine tuning to determine the optimal solution. The applicability and validity of the proposed approach on multi-area economic dispatch problems are presented in two parts. In Part I, two multi-area bench mark problems without fuel options are considered. In Part II, 10 unit system with both multi-area and multi-fuel options is considered. The proposed approach is compared with the results of Incremental Network Flow programming, Spatial Dynamic programming and evolutionary programming approaches. The results show that the EP-LMO gives the optimum generation cost than any Other Methods.
evolutionary computation plays a principal role in intelligent design automation. evolutionary approaches have discovered novel and patentable designs. Nonetheless, evolutionary techniques may lead to designs that lac...
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evolutionary computation plays a principal role in intelligent design automation. evolutionary approaches have discovered novel and patentable designs. Nonetheless, evolutionary techniques may lead to designs that lack robustness. This critical issue is strongly connected to the concept of evolvability. In nature, highly evolvable species tend to be found in rapidly changing environments. Such species can be considered robust against environmental changes. Consequently, to create robust engineering designs it could be beneficial to use variable, rather than fixed, fitness criteria. In this paper, we study the performance of an evolutionary programming algorithm with periodical switching between goals, which are selected randomly from a set of related goals. It is shown by a dual-objective filter optimization example that altering goals may improve evolvability to a fixed goal by enhancing the dynamics of solution population, and guiding the search to areas where improved solutions are likely to be found. Our reference algorithm with a single goal is able to find solutions with competitive fitness, but these solutions are results of premature convergence, because they are poorly evolvable. By using the same algorithm with switching goals, we can extend the productive search length considerably;both the fitness and robustness of such designs are improved. (C) 2009 Elsevier Inc. All rights reserved.
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