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Evolutionary Computation, IEEE Transactions on

Issue 1 • Date Apr 1999

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Displaying Results 1 - 5 of 5
  • System design by constraint adaptation and differential evolution

    Page(s): 22 - 34
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (528 KB)  

    A simple optimization procedure for constraint-based problems is described which works with a simplified cost function or even without one. The simplification of the problem formulation makes this method particularly attractive. The new method lends itself to parallel computation and is well suited for constraint satisfaction, constrained optimization, and design centering problems. A further asset is its self-steering property which makes the new method easy to use View full abstract»

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  • A MS-GS VQ codebook design for wireless image communication using genetic algorithms

    Page(s): 35 - 52
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1436 KB)  

    An image compression technique is proposed that attempts to achieve both robustness to transmission bit errors common to wireless image communication, as well as sufficient visual quality of the reconstructed images. Error robustness is achieved by using biorthogonal wavelet subband image coding with multistage gain-shape vector quantization (MS-GS VQ) which uses three stages of signal decomposition in an attempt to reduce the effect of transmission bit errors by distributing image information among many blocks. Good visual quality of the reconstructed images is obtained by applying genetic algorithms (GAs) to codebook generation to produce reconstruction capabilities that are superior to the conventional techniques. The proposed decomposition scheme also supports the use of GAs because decomposition reduces the problem size. Some simulations for evaluating the performance of the proposed coding scheme on both transmission bit errors and distortions of the reconstructed images are performed. Simulation results show that the proposed MS-GS VQ with good codebooks designed by GAs provides not only better robustness to transmission bit errors but also higher peak signal-to-noise ratio even under high bit error rate conditions View full abstract»

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  • An orthogonal genetic algorithm for multimedia multicast routing

    Page(s): 53 - 62
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (328 KB)  

    Many multimedia communication applications require a source to send multimedia information to multiple destinations through a communication network. To support these applications, it is necessary to determine a multicast tree of minimal cost to connect the source node to the destination nodes subject to delay constraints on multimedia communication. This problem is known as multimedia multicast routing and has been proved to be NP-complete. The paper proposes an orthogonal genetic algorithm for multimedia multicast routing. Its salient feature is to incorporate an experimental design method called orthogonal design into the crossover operation. As a result, it can search the solution space in a statistically sound manner and it is well suited for parallel implementation and execution. We execute the orthogonal genetic algorithm to solve two sets of benchmark test problems. The results indicate that for practical problem sizes, the orthogonal genetic algorithm can find near optimal solutions within moderate numbers of generations View full abstract»

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  • A multistage evolutionary algorithm for the timetable problem

    Page(s): 63 - 74
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (276 KB)  

    It is well known that timetabling problems can be very difficult to solve, especially when dealing with particularly large instances. Finding near-optimal results can prove to be extremely difficult, even when using advanced search methods such as evolutionary algorithms (EAs). The paper presents a method of decomposing larger problems into smaller components, each of which is of a size that the EA can effectively handle. Various experimental results using this method show that not only can the execution time be considerably reduced but also that the presented method can actually improve the quality of the solutions View full abstract»

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  • Image segmentation using evolutionary computation

    Page(s): 1 - 21
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1828 KB)  

    Image segmentation denotes a process by which a raw input image is partitioned into nonoverlapping regions such that each region is homogeneous and the union of any two adjacent regions is heterogeneous. A segmented image is considered to be the highest domain-independent abstraction of an input image. The image segmentation problem is treated as one of combinatorial optimization. A cost function which incorporates both edge information and region gray-scale uniformity is defined. The cost function is shown to be multivariate with several local minima. The genetic algorithm, a stochastic optimization technique based on evolutionary computation, is explored in the context of image segmentation. A class of hybrid evolutionary optimization algorithms based on a combination of the genetic algorithm and stochastic annealing algorithms such as simulated annealing, microcanonical annealing, and the random cost algorithm is shown to exhibit superior performance as compared with the canonical genetic algorithm. Experimental results on gray-scale images are presented View full abstract»

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Aims & Scope

IEEE Transactions on Evolutionary Computation publishes archival quality original papers in evolutionary computation and related areas including nature-inspired algorithms, population-based methods, and optimization where selection and variation are integral, and hybrid systems where these paradigms are combined. Purely theoretical papers are considered as are application papers that provide general insights into these areas of computation.
 

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Editor-in-Chief
Garrison W. Greenwood, Ph.D. P.E
Portland State University