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IEEE Transactions on Evolutionary Computation

Issue 3 • Sep 2000

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Displaying Results 1 - 8 of 8
  • Stochastic ranking for constrained evolutionary optimization

    Publication Year: 2000, Page(s):284 - 294
    Cited by:  Papers (624)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (448 KB)

    Penalty functions are often used in constrained optimization. However, it is very difficult to strike the right balance between objective and penalty functions. This paper introduces a novel approach to balance objective and penalty functions stochastically, i.e., stochastic ranking, and presents a new view on penalty function methods in terms of the dominance of penalty and objective functions. S... View full abstract»

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  • Two fast tree-creation algorithms for genetic programming

    Publication Year: 2000, Page(s):274 - 283
    Cited by:  Papers (26)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (300 KB)

    Genetic programming is an evolutionary optimization method that produces functional programs to solve a given task. These programs commonly take the form of trees representing LISP s-expressions, and a typical evolutionary run produces a great many of these trees. For this reason, a good tree-generation algorithm is very important to genetic programming. This paper presents two new tree-generation... View full abstract»

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  • Application of genetic programming for multicategory pattern classification

    Publication Year: 2000, Page(s):242 - 258
    Cited by:  Papers (125)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (468 KB)

    Explores the feasibility of applying genetic programming (GP) to multicategory pattern classification problem. GP can discover relationships and express them mathematically. GP-based techniques have an advantage over statistical methods because they are distribution-free, i.e., no prior knowledge is needed about the statistical distribution of the data. GP also automatically discovers the discrimi... View full abstract»

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  • Markov chain models of parallel genetic algorithms

    Publication Year: 2000, Page(s):216 - 226
    Cited by:  Papers (39)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (236 KB)

    Implementations of parallel genetic algorithms (GA) with multiple populations are common, but they introduce several parameters whose effect on the quality of the search is not well understood. Parameters such as the number of populations, their size, the topology of communications, and the migration rate have to be set carefully to reach adequate solutions. This paper presents models that predict... View full abstract»

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  • Solving equations by hybrid evolutionary computation techniques

    Publication Year: 2000, Page(s):295 - 304
    Cited by:  Papers (16)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (500 KB)

    Evolutionary computation techniques have mostly been used to solve various optimization and learning problems. This paper describes a novel application of evolutionary computation techniques to equation solving. Several combinations of evolutionary computation techniques and classical numerical methods are proposed to solve linear and partial differential equations. The hybrid algorithms have been... View full abstract»

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  • Genetic fuzzy learning

    Publication Year: 2000, Page(s):259 - 273
    Cited by:  Papers (100)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (540 KB)

    A hybrid approach to fuzzy supervised learning is presented. It is based on a genetic-neuro learning algorithm. The mixed-genetic coding adopted involves only the premises of the fuzzy rules. The conclusions are derived through a least-squares solution of an over-determined system using the singular value decomposition (SVD) algorithm. The paper presents the results obtained with C++ software call... View full abstract»

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  • Modeling of ship trajectory in collision situations by an evolutionary algorithm

    Publication Year: 2000, Page(s):227 - 241
    Cited by:  Papers (59)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (420 KB)

    For a given circumstance (i.e., a collision situation at sea), a decision support system for navigation should help the operator to choose a proper manoeuvre, teach him good habits, and enhance his general intuition on how to behave in similar situations in the future. By taking into account certain boundaries of the maneuvering region along with information on navigation obstacles and other movin... View full abstract»

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  • Test-case generator for nonlinear continuous parameter optimization techniques

    Publication Year: 2000, Page(s):197 - 215
    Cited by:  Papers (48)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (972 KB)

    The experimental results reported in many papers suggest that making an appropriate a priori choice of an evolutionary method for a nonlinear parameter optimization problem remains an open question. It seems that the most promising approach at this stage of research is experimental, involving the design of a scalable test suite of constrained optimization problems, in which many features could be ... 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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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