This paper considers the design of reduced complexity two-dimensional FIR filters using geneticalgorithms (GAs). Circularly symmetric and diamond shaped low-pass linear phase FIR filters are designed using coefficien...
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This paper considers the design of reduced complexity two-dimensional FIR filters using geneticalgorithms (GAs). Circularly symmetric and diamond shaped low-pass linear phase FIR filters are designed using coefficients comprising the sum or difference of two power-of-two terms. A minimax error criterion is adopted which leads to a minimization of the weighted ripple extrema in both pass and stop bands. The results presented are compared with those obtained using simulated annealing, linear programming and simple rounding of an optimum (continuous) minimax solution. The filters designed using GAs are found to be as good or better than those designed using other methods, with less computational effort than the simulated annealing approach.
The use of a co-ordinate measuring machine (CMM) is wide spread throughout the manufacturing industry. In spite of this, generative inspection planning for a CMM, especially by using artificial intelligence (AI) techn...
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The use of a co-ordinate measuring machine (CMM) is wide spread throughout the manufacturing industry. In spite of this, generative inspection planning for a CMM, especially by using artificial intelligence (AI) techniques, is not well developed. This paper presents an approach of using a genetic algorithm technique to carry out the inspection path planning for a CMM, in a multi-component inspection application. This path planning system applies genetic algorithm theory to establish an optimizer and to develop a learning function.
A hardware based parallel genetic algorithm (PGA), using order-based crossover capable of optimizing a new category of real-time combinatorial problems, is proposed. In order to establish the benefits of the proposed ...
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A hardware based parallel genetic algorithm (PGA), using order-based crossover capable of optimizing a new category of real-time combinatorial problems, is proposed. In order to establish the benefits of the proposed hardware, disk-scheduling is identified as a common real-time optimization problem. After discussing the PGA, details of the disk-scheduling problem is discussed along with conventional solutions. A suitable hardware design for the order-based PGA and timing calculations are presented. Simulation results which contrast conventional and PGA results for disk scheduling are provided, and conclusions are drawn from the results.
A new genetic learning algorithm is described in this paper to perform vector quantization for image compression. The algorithm is initialized by generating several random code-books to create a gene pool. A genetic a...
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A new genetic learning algorithm is described in this paper to perform vector quantization for image compression. The algorithm is initialized by generating several random code-books to create a gene pool. A genetic algorithm is then developed and activated to improve on the performance of the initial code-books. After a certain number of generations, a final code-book is selected in accordance with the operation of the genetic learning algorithm. Experiments show that the proposed algorithm outperforms the competitive learning algorithm for vector quantization of digitized images. Significant improvements as high as over 15% have been achieved by the proposed genetic algorithm. In addition, potential for a new direction of vector quantization and image compression research is opened up by considering the establishment of a gene pool and various designs of crossover breeding and mutation operations in the algorithm design.
Together with MATLAB and SIMULlNK, the genetic algorithm (GA) Toolbox described presents a familiar and unified environment for the control engineer to experiment with and apply GAs to tasks in controlsystems enginee...
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Together with MATLAB and SIMULlNK, the genetic algorithm (GA) Toolbox described presents a familiar and unified environment for the control engineer to experiment with and apply GAs to tasks in controlsystemsengineering. Whilst the GA Toolbox was developed with the emphasis on controlengineering applications, it should prove equally as useful in the general field of GAs, particularly given the range of domain-specific toolboxes available for the MATLAB package.
The design of digital IIR filters is a multistage process, involving the optimization of coefficient values, coefficient wordlengths, structure and section ordering. These are traditionally regarded as separate operat...
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The design of digital IIR filters is a multistage process, involving the optimization of coefficient values, coefficient wordlengths, structure and section ordering. These are traditionally regarded as separate operations, and, as such, can in general only produce filters which are optimal in certain aspects, but not optimal overall. By exploiting the multiple criterion optimization abilities of the genetic Algorithm, we show that it is possible to perform several of these steps simultaneously. This allows the designer to specify the relative importance of each area of the design, for example, the frequency response or roundoff noise effects, thereby permitting the design of filters from a few initial specifications without requiring detailed knowledge of the individual design steps.
This paper presents a methodology for the synthesis of multi-degree of freedom mechanisms using geneticalgorithms. A Five-Bar mechanism is a 2-DOF system which requires two inputs to fully describe the output motion....
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This paper presents a methodology for the synthesis of multi-degree of freedom mechanisms using geneticalgorithms. A Five-Bar mechanism is a 2-DOF system which requires two inputs to fully describe the output motion. In a hybrid mechanism, one of these inputs is supplied by a Constant Velocity (CV) motor and one is supplied by a programmable servo motor. Such configurations can have considerable savings in power consumption when the armature inertia of the servo motor is low when compared to the inertia. In the presented synthesis of such mechanisms, the two inputs required are provided by the CV input and the desired position of the end effector. The genetic Algorithm is used to search for the optimum link lengths and ground point positions to minimize a multi-criteria objective function. The criteria which contribute to the objective function value are the error between the actual path of the end effector and the desired path, the mobility of the mechanism and the RMS value of the servo motor displacements.
We describe a hybrid GA approach to the no-wait flowshop scheduling problem using the minimize makespan as objective function. The GA approach described produces comparable or better results on benchmark flowshop prob...
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We describe a hybrid GA approach to the no-wait flowshop scheduling problem using the minimize makespan as objective function. The GA approach described produces comparable or better results on benchmark flowshop problems than known heuristic methods. The representation used is a variant of one used on GA treatments of the TSP. The genetic operators include Edge Recombination and three additional operators designed on analogy to known heuristic methods. Experiments show that these new operators are very effective in enhancing the performance of the traditional GA. The present approach also promises to effectively address the general job-shop problem.
Many computer problems today need the computer power that is only available using large scale parallel processing. For a significant number of these problems, the density of the global communications between the indiv...
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Many computer problems today need the computer power that is only available using large scale parallel processing. For a significant number of these problems, the density of the global communications between the individual processors dominates the performance of the whole parallel implementation on a distributed memory multiprocessor system. In these cases the design of the interconnection network for the processors is known to play a significant part in the efficient implementation of real problems. Networks of processors connected as an irregular configuration have the advantage that they satisfy the best known criteria for producing configurations that perform well on real applications. This paper presents a new genetic algorithm for the generation of optimal irregular configurations, which is a challenging problem since there is no obvious representation which allows all the main genetic operations to be implemented effectively.
The complexity of data on the anatomical structure of the cat visual system has prompted the search for effective methods of data analysis. The overall structure of the data has been analyzed using an optimization pro...
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The complexity of data on the anatomical structure of the cat visual system has prompted the search for effective methods of data analysis. The overall structure of the data has been analyzed using an optimization procedure. The procedure employed a modified annealing algorithm, which was integrated into the CANTOR processing environment, and searched for optimal hierarchical orderings. The flexible network processor developed for determining optimal hierarchies for the neuroanatomical data might also be suitable for analyzing other real-world relational problems, such as the optimal structuring of other complex data or modeling the structures of macromolecules.
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