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Evolutionary Computation, IEEE Transactions on

Issue 3 • Date June 2008

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Displaying Results 1 - 18 of 18
  • Table of contents

    Publication Year: 2008 , Page(s): C1
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  • IEEE Transactions on Evolutionary Computation publication information

    Publication Year: 2008 , Page(s): C2
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  • A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA

    Publication Year: 2008 , Page(s): 269 - 283
    Cited by:  Papers (121)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (814 KB)  

    This paper describes a simulated annealing based multiobjective optimization algorithm that incorporates the concept of archive in order to provide a set of tradeoff solutions for the problem under consideration. To determine the acceptance probability of a new solution vis-a-vis the current solution, an elaborate procedure is followed that takes into account the domination status of the new solution with the current solution, as well as those in the archive. A measure of the amount of domination between two solutions is also used for this purpose. A complexity analysis of the proposed algorithm is provided. An extensive comparative study of the proposed algorithm with two other existing and well-known multiobjective evolutionary algorithms (MOEAs) demonstrate the effectiveness of the former with respect to five existing performance measures, and several test problems of varying degrees of difficulty. In particular, the proposed algorithm is found to be significantly superior for many objective test problems (e.g., 4, 5, 10, and 15 objective problems), while recent studies have indicated that the Pareto ranking-based MOEAs perform poorly for such problems. In a part of the investigation, comparison of the real-coded version of the proposed algorithm is conducted with a very recent multiobjective simulated annealing algorithm, where the performance of the former is found to be generally superior to that of the latter. View full abstract»

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  • Registration of CT and Intraoperative 3-D Ultrasound Images of the Spine Using Evolutionary and Gradient-Based Methods

    Publication Year: 2008 , Page(s): 284 - 296
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1561 KB)  

    A system for the registration of computed tomography and 3-D intraoperative ultrasound images is presented. Three gradient-based methods and one evolutionary algorithm are compared with regard to their suitability to solve this image registration problem. The system has been developed for pedicle screw insertion during spinal surgery. With clinical preoperative and intraoperative data, it is demonstrated that precise registration is possible within a realistic range of initial misalignment. Significant differences can be observed between the optimization methods. The covariance matrix adaptation evolution strategy shows the best overall performance, only four of 12 000 registration trials with patient data failed to register correctly. View full abstract»

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  • An Evolutionary Algorithm to Find Associations in Dense Genetic Maps

    Publication Year: 2008 , Page(s): 297 - 306
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (569 KB)  

    Discovering the genetic basis of common human diseases will be assisted by large-scale association studies with a large number of individuals and genetic markers, such as single-nucleotide polymorphisms (SNPs). The potential size of the data and the resulting model space require the development of efficient methodology to unravel associations between epidemiological outcomes and SNPs in dense genetic maps. We apply an evolutionary algorithm (EA) to construct models consisting of logic trees. These trees are Boolean expressions involving nodes that contain strings of SNPs in high linkage disequilibrium (LD), that is, SNPs that are highly correlated with each other. At each generation of the algorithm, a population of logic tree models is modified using selection, crossover, and mutation moves. Logic trees are selected for the next generation using a fitness function based on the marginal likelihood in a Bayesian regression framework. Mutation and crossover moves use LD measures to propose changes to the trees, and facilitate the movement through the model space. We demonstrate our method on data from a candidate gene study of quantitative genetic variation. View full abstract»

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  • On the Scalability of Real-Coded Bayesian Optimization Algorithm

    Publication Year: 2008 , Page(s): 307 - 322
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (705 KB)  

    Estimation of distribution algorithms (EDAs) are major tools in evolutionary optimization. They have the ability to uncover the hidden regularities of problems and then exploit them for effective search. Real-coded Bayesian optimization algorithm (rBOA) which brings the power of discrete BOA to bear upon the continuous domain has been regarded as a milestone in the field of numerical optimization. It has been empirically observed that the rBOA solves, with subquadratic scaleup behavior, numerical optimization problems of bounded difficulty. This underlines the scalability of rBOA (at least) in practice. However, there is no firm theoretical basis for this scalability. The aim of this paper is to carry out a theoretical analysis of the scalability of rBOA in the context of additively decomposable problems with real-valued variables. The scalability is measured by the growth of the number of fitness function evaluations (in order to reach the optimum) with the size of the problem. The total number of evaluations is computed by multiplying the population size for learning a correct probabilistic model (i.e., population complexity) and the number of generations before convergence, (i.e., convergence time complexity). Experimental results support the scalability model of rBOA. The rBOA shows a subquadratic (in problem size) scalability for uniformly scaled decomposable problems. View full abstract»

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  • Dominance-Based Multiobjective Simulated Annealing

    Publication Year: 2008 , Page(s): 323 - 342
    Cited by:  Papers (32)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1981 KB)  

    Simulated annealing is a provably convergent optimizer for single-objective problems. Previously proposed multiobjective extensions have mostly taken the form of a single-objective simulated annealer optimizing a composite function of the objectives. We propose a multiobjective simulated annealer utilizing the relative dominance of a solution as the system energy for optimization, eliminating problems associated with composite objective functions. We also propose a method for choosing perturbation scalings promoting search both towards and across the Pareto front. We illustrate the simulated annealer's performance on a suite of standard test problems and provide comparisons with another multiobjective simulated annealer and the NSGA-II genetic algorithm. The new simulated annealer is shown to promote rapid convergence to the true Pareto front with a good coverage of solutions across it comparing favorably with the other algorithms. An application of the simulated annealer to an industrial problem, the optimization of a code-division-multiple access (CDMA) mobile telecommunications network's air interface, is presented and the simulated annealer is shown to generate nondominated solutions with an even and dense coverage that outperforms single objective genetic algorithm optimizers. View full abstract»

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  • Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria Using Interactive Genetic Algorithms

    Publication Year: 2008 , Page(s): 343 - 354
    Cited by:  Papers (19)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (725 KB)  

    This paper emphasizes the necessity of formally bringing qualitative and quantitative criteria of ergonomic design together, and provides a novel complementary design framework with this aim. Within this framework, different design criteria are viewed as optimization objectives, and design solutions are iteratively improved through the cooperative efforts of computer and user. The framework is rooted in multiobjective optimization, genetic algorithms, and interactive user evaluation. Three different algorithms based on the framework are developed, and tested with an ergonomic chair design problem. The parallel and multiobjective approaches show promising results in fitness convergence, design diversity, and user satisfaction metrics. View full abstract»

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  • Function Approximation With XCS: Hyperellipsoidal Conditions, Recursive Least Squares, and Compaction

    Publication Year: 2008 , Page(s): 355 - 376
    Cited by:  Papers (17)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1343 KB)  

    An important strength of learning classifier systems (LCSs) lies in the combination of genetic optimization techniques with gradient-based approximation techniques. The chosen approximation technique develops locally optimal approximations, such as accurate classification estimates, Q-value predictions, or linear function approximations. The genetic optimization technique is designed to distribute these local approximations efficiently over the problem space. Together, the two components develop a distributed, locally optimized problem solution in the form of a population of expert rules, often called classifiers. In function approximation problems, the XCSF classifier system develops a problem solution in the form of overlapping, piecewise linear approximations. This paper shows that XCSF performance on function approximation problems additively benefits from: 1) improved representations; 2) improved genetic operators; and 3) improved approximation techniques. Additionally, this paper introduces a novel closest classifier matching mechanism for the efficient compaction of XCS's final problem solution. The resulting compaction mechanism can boil the population size down by 90% on average, while decreasing prediction accuracy only marginally. Performance evaluations show that the additional mechanisms enable XCSF to reliably, accurately, and compactly approximate even seven dimensional functions. Performance comparisons with other, heuristic function approximation techniques show that XCSF yields competitive or even superior noise-robust performance. View full abstract»

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  • An Evolutionary Algorithm Approach to Optimal Ensemble Classifiers for DNA Microarray Data Analysis

    Publication Year: 2008 , Page(s): 377 - 388
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1100 KB)  

    In general, the analysis of microarray data requires two steps: feature selection and classification. From a variety of feature selection methods and classifiers, it is difficult to find optimal ensembles composed of any feature-classifier pairs. This paper proposes a novel method based on the evolutionary algorithm (EA) to form sophisticated ensembles of features and classifiers that can be used to obtain high classification performance. In spite of the exponential number of possible ensembles of individual feature-classifier pairs, an EA can produce the best ensemble in a reasonable amount of time. The chromosome is encoded with real values to decide the weight for each feature-classifier pair in an ensemble. Experimental results with two well-known microarray datasets in terms of time and classification rate indicate that the proposed method produces ensembles that are superior to individual classifiers, as well as other ensembles optimized by random and greedy strategies. View full abstract»

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  • An Explicit Selection Intensity of Tournament Selection-Based Genetic Algorithms

    Publication Year: 2008 , Page(s): 389 - 391
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    An explicit expression is derived for the selection intensity of genetic algorithms discussed by Ahn and Ramakrishna, 2003. The expression involves a well-known special function. The practical issues of using the expression (including a simple approximation) are discussed. View full abstract»

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  • Errata to “RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm” [Feb 08 41-63]

    Publication Year: 2008 , Page(s): 392
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (113 KB)  

    In the above titled paper (ibid., vol. 12, no. 1, pp. 41-63, Feb. 08), Fig. 20 was wrong. Its replacement is presented here. View full abstract»

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  • IEEE Symposium on Computational Intelligence and Games

    Publication Year: 2008 , Page(s): 393
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    Freely Available from IEEE
  • IEEE Congress on Evolutionary Computation

    Publication Year: 2008 , Page(s): 394
    Save to Project icon | Request Permissions | PDF file iconPDF (628 KB)  
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  • Put your technology leadership in writing [advertisement]

    Publication Year: 2008 , Page(s): 395
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  • Order form for reprints

    Publication Year: 2008 , Page(s): 396
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2008 , Page(s): C3
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  • IEEE Transactions on Evolutionary Computation Information for authors

    Publication Year: 2008 , Page(s): C4
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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.
 

Full Aims & Scope

Meet Our Editors

Editor-in-Chief

Dr. Kay Chen Tan (IEEE Fellow)

Department of Electrical and Computer Engineering

National University of Singapore

Singapore 117583

Email: eletankc@nus.edu.sg

Website: http://vlab.ee.nus.edu.sg/~kctan