By Topic

IEEE Transactions on Evolutionary Computation

Issue 1 • Date Apr 2000

Filter Results

Displaying Results 1 - 7 of 7
  • Visual routines for eye location using learning and evolution

    Publication Year: 2000, Page(s):73 - 82
    Cited by:  Papers (32)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (512 KB)

    Eye location is used as a test bed for developing navigation routines implemented as visual routines within the framework of adaptive behavior-based AI. The adaptive eye location approach seeks first where salient objects are, and then what their identity is. Specifically, eye location involves: 1) the derivation of the saliency attention map, and 2) the possible classification of salient location... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Interval-valued GA-P algorithms

    Publication Year: 2000, Page(s):64 - 72
    Cited by:  Papers (18)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (260 KB)

    When genetic programming (GP) methods are applied to solve symbolic regression problems, we obtain a point estimate of a variable, but it is not easy to calculate an associated confidence interval. We designed an interval arithmetic-based model that solves this problem. Our model extends a hybrid technique, the GA-P method, that combines genetic algorithms and genetic programming. Models based on ... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Gradual distributed real-coded genetic algorithms

    Publication Year: 2000, Page(s):43 - 63
    Cited by:  Papers (115)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (600 KB)

    A major problem in the use of genetic algorithms is premature convergence. One approach for dealing with this problem is the distributed genetic algorithm model. Its basic idea is to keep, in parallel, several subpopulations that are processed by genetic algorithms, with each one being independent of the others. Making distinctions between the subpopulations by applying genetic algorithms with dif... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A study of the Lamarckian evolution of recurrent neural networks

    Publication Year: 2000, Page(s):31 - 42
    Cited by:  Papers (21)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (276 KB)

    Training neural networks by evolutionary search can require a long computation time. In certain situations, using Lamarckian evolution, local search and evolutionary search can complement each other to yield a better training algorithm. This paper demonstrates the potential of this evolutionary-learning synergy by applying it to train recurrent neural networks in an attempt to resolve a long-term ... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Statistical distribution of the convergence time of evolutionary algorithms for long-path problems

    Publication Year: 2000, Page(s):16 - 30
    Cited by:  Papers (11)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (732 KB)

    The behavior of a (1+1)-ES process on Rudolph's binary long k paths is investigated extensively in the asymptotic framework with respect to string length l. First, the case of k=lα is addressed. For α⩾1/2, we prove that the long k path is a long path for the (1+1)-ES in the sense that the process follows the entire path with no shortcuts, resulting in an exponential expe... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A comparison of predictive measures of problem difficulty in evolutionary algorithms

    Publication Year: 2000, Page(s):1 - 15
    Cited by:  Papers (69)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (480 KB)

    This paper studies a number of predictive measures of problem difficulty, among which epistasis variance and fitness distance correlation are the most widely known. Our approach is based on comparing the reference class of a measure to a number of known easy function classes. First, we generalize the reference classes of fitness distance correlation and epistasis variance, and construct a new pred... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A genetic algorithm approach to image reconstruction in electrical impedance tomography

    Publication Year: 2000, Page(s):83 - 88
    Cited by:  Papers (26)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (420 KB)

    Electrical impedance tomography (EIT) determines the resistivity distribution inside an inhomogeneous object by means of voltage and/or current measurements conducted at the object boundary. A genetic algorithm (GA) approach is proposed for the solution of the EIT inverse problem, in particular for the reconstruction of “static” images. Results of numerical experiments of EIT solved by... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.

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