This paper presents general finite impulse response (FIR) digital filter design with asymmetric coefficients to approximate passband and stopband magnitude responses and constant passband group delay specifications us...
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This paper presents general finite impulse response (FIR) digital filter design with asymmetric coefficients to approximate passband and stopband magnitude responses and constant passband group delay specifications using an evolutionary optimization algorithm called the Interactive Self Learning Algorithm (ISLA). Lowpass and bandpass digital filters are chosen and their design results are shown to demonstrate the effectiveness of the approach. Results indicate that passband and stopband peak magnitude errors and passband peak group delay error can be designed to approximate given specifications.
Container Loading Problems (CLPs) deal with determination of the optimal pattern for packing boxes into a given container usually with respect to the maximal utilization of the total container volume. On the other han...
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ISBN:
(纸本)9781450349390
Container Loading Problems (CLPs) deal with determination of the optimal pattern for packing boxes into a given container usually with respect to the maximal utilization of the total container volume. On the other hand, it is also important to maximize the utilization of the maximal container weight for which is paid when buying a shipment service. In this paper we analyze two genetic algorithms specially adopted to solve CLP. One of them is based on the Genetic Algorithm (GA) and is suitable to solve single-objective CLPs, while another one is based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), suitable for solution of CLP by simultaneously considering both of the above mentioned objectives. The algorithms have been experimentally investigated by solving various CLP instances of different complexity. The obtained results showed that simultaneous consideration of both objectives using the proposed multi-objective optimization algorithm gives better results in utilization of container volume when solving complex CLP instances.
A large number of infeasible solutions often occur in population of evolutionary Computation (EC) solving the constraint combinatorial optimization problems. The greater the number of infeasible solutions in the popul...
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A large number of infeasible solutions often occur in population of evolutionary Computation (EC) solving the constraint combinatorial optimization problems. The greater the number of infeasible solutions in the population, the worse the performance of ECto search the solution, in the worst case, the algorithm ceases to run. The existing methods, penalty function or multi-objective optimization, can relieve partly the worst case of EC to run. However, they are actually to restrain the infeasible solutions surviving in population, the performance of the EC is not improved. In this study we propose an approach using an important feature of the infeasible solutions in Genetic algorithms (GA). The approach can not only solve the problem of algorithm ceases to run, but also improve effectively the performance of genetic algorithms searching the optimal solution. From examination of the proposed method on multidimensional knapsack problems, the application of method is effective to solve the problem of algorithm ceases to run as well as to improve clearly the performance of GA.
This paper demonstrates the structural optimization using evolutionary algorithms in a chalcogenide glass waveguide. Four features are taken into consideration while optimizing the waveguide structure, they include: s...
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ISBN:
(纸本)9780819488459
This paper demonstrates the structural optimization using evolutionary algorithms in a chalcogenide glass waveguide. Four features are taken into consideration while optimizing the waveguide structure, they include: single-mode, low dispersion, high nonlinearity and low loss. A set of waveguide structures which meet the design criteria are shown in the paper. The best structure enhances the nonlinear coefficient to 26000 /W/km at telecom wavelength. In this work, we demonstrate the methodology used to optimize waveguide as well as the procedure of conducting the experiment.
Reversible circuits, i.e. circuits which map each possible input vector to a unique output vector, build the basis for emerging applications e.g. in the domain of low-power design or quantum computation. As a result, ...
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ISBN:
(数字)9783642205200
ISBN:
(纸本)9783642205194
Reversible circuits, i.e. circuits which map each possible input vector to a unique output vector, build the basis for emerging applications e.g. in the domain of low-power design or quantum computation. As a result, researchers developed various approaches for synthesis of this kind of logic. In this paper, we consider the ESOP-based synthesis method. Here, functions given as Exclusive Sum of Products (ESOPs) are realized. In contrast to conventional circuit optimization, the quality of the resulting circuits depends thereby not only on the number of product terms, but on further criteria as well. In this paper, we present an approach based on an evolutionary algorithm which optimizes the function description with respect to these criteria. Instead of ESOPs, Pseudo Kronecker Expression (PSDKRO) are thereby utilized enabling minimization within reasonable time bounds. Experimental results confirm that the proposed approach enables the realization of circuits with significantly less cost.
Many engineering applications can be approached as optimization problems whose solution commonly involves the execution of computational expensive objective functions. Recently, evolutionary algorithms ( EAs) are gain...
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Many engineering applications can be approached as optimization problems whose solution commonly involves the execution of computational expensive objective functions. Recently, evolutionary algorithms ( EAs) are gaining popularity for solving complex problems that are encountered in many disciplines, delivering a more robust and effective way to locate global optima in comparison to classical optimization methods. However, applying EA's to real-world problems demands a large number of function evaluations before delivering a satisfying result. Under such circumstances, several EAs have been adapted to reduce the number of function evaluations by using alternative models to substitute the original objective function. Despite such approaches employ a reduced number of function evaluations, the use of alternative models seriously affects their original EA search capacities and their solution accuracy. Recently, a new evolutionary method called the Adaptive Population with Reduced Evaluations ( APRE) has been proposed to solve several image processing problems. APRE reduces the number of function evaluations through the use of two mechanisms: ( 1) The dynamic adaptation of the population and ( 2) the incorporation of a fitness calculation strategy, which decides when it is feasible to calculate or only estimate new generated individuals. As a result, the approach can substantially reduce the number of function evaluations, yet preserving the good search capabilities of an evolutionary approach. In this paper, the performance of APRE as a global optimization algorithm is presented. In order to illustrate the proficiency and robustness of APRE, it has been compared to other approaches that have been previously conceived to reduce the number of function evaluations. The comparison examines several standard benchmark functions, which are commonly considered within the EA field. Conducted simulations have confirmed that the proposed method achieves the best balance over its
The decomposition-based method has been recognized as a major approach for multiobjective optimization. It decomposes a multi-objective optimization problem into several singleobjective optimization subproblems, each ...
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We present a number of bounds on convergence time for two elitist population-based evolutionary algorithms using a recombination operator k-Bit-Swap and a mainstream Randomized Local Search algorithm. We study the eff...
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ISBN:
(纸本)9789898425836
We present a number of bounds on convergence time for two elitist population-based evolutionary algorithms using a recombination operator k-Bit-Swap and a mainstream Randomized Local Search algorithm. We study the effect of distribution of elite species and population size.
Metabolic pathway building is an active field of research, necessary to understand and manipulate the metabolism of organisms. There are different approaches, mainly based on classical search methods, to find linear s...
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Metabolic pathway building is an active field of research, necessary to understand and manipulate the metabolism of organisms. There are different approaches, mainly based on classical search methods, to find linear sequences of reactions linking two compounds. However, an important limitation of these methods is the exponential increase of search trees when a large number of compounds and reactions is considered. Besides, such models do not take into account all substrates for each reaction during the search, leading to solutions that lack biological feasibility in many cases. This work proposes a new evolutionary algorithm that allows searching not only linear, but also branched metabolic pathways, formed by feasible reactions that relate multiple compounds simultaneously. Tests performed using several sets of reactions show that this algorithm is able to find feasible linear and branched metabolic pathways. (C) 2016 Elsevier Ireland Ltd. All rights reserved.
There are some problems in the implementation of adaptive systems, which are the design complexity related to the problems. In this paper, through analyze the structure of adaptive systems, we divide the evolutionary ...
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There are some problems in the implementation of adaptive systems, which are the design complexity related to the problems. In this paper, through analyze the structure of adaptive systems, we divide the evolutionary system into two parts, one is the evolutionary algorithms kernel and the other is the fitness calculator. In the implementation process, the evolutionary algorithms kernel is implementation using the state machine which described by VHDL language, and the population size, the length of chromosome and other parameters can be configured. We implement the kernel based on Xilinx Virtex-V1000FG680 and use the test problem to verify the effectiveness of our design. (c) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS 2011]
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