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Evolutionary Computation, IEEE Transactions on

Issue 4 • Date Nov. 2000

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Displaying Results 1 - 10 of 10
  • Acknowledgment to reviewers

    Publication Year: 2000 , Page(s): 394 - 395
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    Freely Available from IEEE
  • Author index

    Publication Year: 2000 , Page(s): 396 - 398
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    Freely Available from IEEE
  • Subject index

    Publication Year: 2000 , Page(s): 398 - 402
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    Freely Available from IEEE
  • Designing classifier fusion systems by genetic algorithms

    Publication Year: 2000 , Page(s): 327 - 336
    Cited by:  Papers (65)  |  Patents (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (376 KB)  

    We suggest two simple ways to use a genetic algorithm (GA) to design a multiple-classifier system. The first GA version selects disjoint feature subsets to be used by the individual classifiers, whereas the second version selects (possibly) overlapping feature subsets, and also the types of the individual classifiers. The two GAs have been tested with four real data sets: heart, Satimage, letters, and forensic glasses. We used three-classifier systems and basic types of individual classifiers (the linear and quadratic discriminant classifiers and the logistic classifier). The multiple-classifier systems designed with the two GAs were compared against classifiers using: all features; the best feature subset found by the sequential backward selection method; and the best feature subset found by a CA. The GA design can be made less prone to overtraining by including penalty terms in the fitness function accounting for the number of features used. View full abstract»

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  • Evolutionary ensembles with negative correlation learning

    Publication Year: 2000 , Page(s): 380 - 387
    Cited by:  Papers (98)  |  Patents (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (128 KB)  

    Based on negative correlation learning and evolutionary learning, this paper presents evolutionary ensembles with negative correlation learning (EENCL) to address the issues of automatic determination of the number of individual neural networks (NNs) in an ensemble and the exploitation of the interaction between individual NN design and combination. The idea of EENCL is to encourage different individual NNs in the ensemble to learn different parts or aspects of the training data so that the ensemble can learn better the entire training data. The cooperation and specialization among different individual NNs are considered during the individual NN design. This provides an opportunity for different NNs to interact with each other and to specialize. Experiments on two real-world problems demonstrate that EENCL can produce NN ensembles with good generalization ability. View full abstract»

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  • A hybrid evolutionary approach for solving constrained optimization problems over finite domains

    Publication Year: 2000 , Page(s): 353 - 372
    Cited by:  Papers (5)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (828 KB)  

    A novel approach for the integration of evolution programs and constraint-solving techniques over finite domains is presented. This integration provides a problem-independent optimization strategy for large-scale constrained optimization problems over finite domains. In this approach, genetic operators are based on an arc-consistency algorithm, and chromosomes are arc-consistent portions of the search space of the problem. The paper describes the main issues arising in this integration: chromosome representation and evaluation, selection and replacement strategies, and the design of genetic operators. We also present a parallel execution model for a distributed memory architecture of the previous integration. We have adopted a global parallelization approach that preserves the properties, behavior, and fundamentals of the sequential algorithm. Linear speedup is achieved since genetic operators are coarse grained as they perform a search in a discrete space carrying out arc consistency. The implementation has been tested on a GRAY T3E multiprocessor using a complex constrained optimization problem. View full abstract»

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  • The computational complexity of N-K fitness functions

    Publication Year: 2000 , Page(s): 373 - 379
    Cited by:  Papers (6)
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    N-K fitness landscapes have been used widely as examples and test functions in the field of evolutionary computation. We investigate the computational complexity of the problem of optimizing the N-K fitness functions and related fitness functions. We give an algorithm to optimize adjacent-model N-K fitness functions, which is polynomial in N. We show that the decision problem corresponding to optimizing random-model N-K fitness functions is NP-complete for K>1, and is polynomial for K=1. If the restriction that the ith component function depends on the ith bit is removed, then the problem is NP-complete, even for K=1. We also give a polynomial-time approximation algorithm for the arbitrary-model N-K optimization problem. View full abstract»

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  • Application of evolutionary programming to adaptive regularization in image restoration

    Publication Year: 2000 , Page(s): 309 - 326
    Cited by:  Papers (8)
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    Image restoration is a difficult problem due to the ill-conditioned nature of the associated inverse filtering operation, which requires regularization techniques. The choice of the corresponding regularization parameter is thus an important issue since an incorrect choice would either lead to noisy appearances in the smooth regions or excessive blurring of the textured regions. In addition, this choice has to be made adaptively across, different image regions to ensure the best subjective quality for the restored image. We employ evolutionary programming (EP) to solve this adaptive regularization problem by generating a population of potential regularization strategies, and allowing them to compete under a new error measure which characterizes a large class of images in terms of their local correlational properties. The nonavailability of explicit gradient information for this measure motivates the adoption of EP techniques for its optimization, which allows efficient search at multiple error surface points. The adoption of EP also allows the broadening of the range of possible cost functions for image processing so that we can choose the most relevant function rather than the most tractable one for a particular image processing application. View full abstract»

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  • Resource sharing and coevolution in evolving cellular automata

    Publication Year: 2000 , Page(s): 388 - 393
    Cited by:  Papers (17)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (168 KB)  

    Coevolution, between a population of candidate solutions and a population of test cases, has received increasing attention as a promising biologically inspired method for improving the performance of evolutionary computation techniques. However, the results of studies of coevolution have been mixed. One of the seemingly more impressive results to date was the improvement via coevolution demonstrated by Juille and Pollack (1998) on evolving cellular automata to perform a classification task. Their study, however, like most other studies on coevolution, did not investigate the mechanisms giving rise to the observed improvements. In this paper, we probe more deeply into the reasons for these observed improvements and present empirical evidence that, in contrast to what was claimed by Juille and Pollack, much of the improvement seen was due to their "resource sharing" technique rather than to coevolution. We also present empirical evidence that resource sharing works, at least in part, by preserving diversity in the population. View full abstract»

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  • Fitness landscape analysis and memetic algorithms for the quadratic assignment problem

    Publication Year: 2000 , Page(s): 337 - 352
    Cited by:  Papers (133)
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    In this paper, a fitness landscape analysis for several instances of the quadratic assignment problem (QAP) is performed, and the results are used to classify problem instances according to their hardness for local search heuristics and meta-heuristics based on local search. The local properties of the fitness landscape are studied by performing an autocorrelation analysis, while the global structure is investigated by employing a fitness distance correlation analysis. It is shown that epistasis, as expressed by the dominance of the flow and distance matrices of a QAP instance, the landscape ruggedness in terms of the correlation length of a landscape, and the correlation between fitness and distance of local optima in the landscape together are useful for predicting the performance of memetic algorithms-evolutionary algorithms incorporating local search (to a certain extent). Thus, based on these properties, a favorable choice of recombination and/or mutation operators can be found. Experiments comparing three different evolutionary operators for a memetic algorithm 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.
 

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