By Topic

IEEE Transactions on Evolutionary Computation

Issue 3 • Date June 2005

Filter Results

Displaying Results 1 - 13 of 13
  • Table of contents

    Publication Year: 2005, Page(s): c1
    Cited by:  Papers (1)
    Request permission for commercial reuse | PDF file iconPDF (31 KB)
    Freely Available from IEEE
  • IEEE Transactions on Evolutionary Computation publication information

    Publication Year: 2005, Page(s): c2
    Request permission for commercial reuse | PDF file iconPDF (35 KB)
    Freely Available from IEEE
  • Training genetic programming on half a million patterns: an example from anomaly detection

    Publication Year: 2005, Page(s):225 - 239
    Cited by:  Papers (61)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (720 KB) | HTML iconHTML

    The hierarchical RSS-DSS algorithm is introduced for dynamically filtering large datasets based on the concepts of training pattern age and difficulty, while utilizing a data structure to facilitate the efficient use of memory hierarchies. Such a scheme provides the basis for training genetic programming (GP) on a data set of half a million patterns in 15 min. The method is generic, thus, not spec... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Observing the evolution of neural networks learning to play the game of Othello

    Publication Year: 2005, Page(s):240 - 251
    Cited by:  Papers (23)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (656 KB) | HTML iconHTML

    A study was conducted to find out how game-playing strategies for Othello (also known as reversi) can be learned without expert knowledge. The approach used the coevolution of a fixed-architecture neural-network-based evaluation function combined with a standard minimax search algorithm. Comparisons between evolving neural networks and computer players that used deterministic strategies allowed ev... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A genetic algorithm for searching spatial configurations

    Publication Year: 2005, Page(s):252 - 270
    Cited by:  Papers (11)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1040 KB) | HTML iconHTML

    Searching spatial configurations is a particular case of maximal constraint satisfaction problems, where constraints expressed by spatial and nonspatial properties guide the search process. In the spatial domain, binary spatial relations are typically used for specifying constraints while searching spatial configurations. Searching configurations is particularly intractable when configurations are... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Cooperative coevolution of artificial neural network ensembles for pattern classification

    Publication Year: 2005, Page(s):271 - 302
    Cited by:  Papers (93)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2592 KB) | HTML iconHTML

    This paper presents a cooperative coevolutive approach for designing neural network ensembles. Cooperative coevolution is a recent paradigm in evolutionary computation that allows the effective modeling of cooperative environments. Although theoretically, a single neural network with a sufficient number of neurons in the hidden layer would suffice to solve any problem, in practice many real-world ... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Evolutionary optimization in uncertain environments-a survey

    Publication Year: 2005, Page(s):303 - 317
    Cited by:  Papers (526)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (480 KB) | HTML iconHTML

    Evolutionary algorithms often have to solve optimization problems in the presence of a wide range of uncertainties. Generally, uncertainties in evolutionary computation can be divided into the following four categories. First, the fitness function is noisy. Second, the design variables and/or the environmental parameters may change after optimization, and the quality of the obtained optimal soluti... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Adaptation technique for integrating genetic programming and reinforcement learning for real robots

    Publication Year: 2005, Page(s):318 - 333
    Cited by:  Papers (17)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1912 KB) | HTML iconHTML

    We propose an integrated technique of genetic programming (GP) and reinforcement learning (RL) to enable a real robot to adapt its actions to a real environment. Our technique does not require a precise simulator because learning is achieved through the real robot. In addition, our technique makes it possible for real robots to learn effective actions. Based on this proposed technique, we acquire ... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Explore IEL IEEE's most comprehensive resource

    Publication Year: 2005, Page(s): 334
    Request permission for commercial reuse | PDF file iconPDF (341 KB)
    Freely Available from IEEE
  • Celebrating the vitality of technology the Proceedings of the IEEE [advertisement]

    Publication Year: 2005, Page(s): 335
    Request permission for commercial reuse | PDF file iconPDF (324 KB)
    Freely Available from IEEE
  • IEEE Member Digital Library

    Publication Year: 2005, Page(s): 336
    Request permission for commercial reuse | PDF file iconPDF (179 KB)
    Freely Available from IEEE
  • IEEE Computational Intelligence Society Information

    Publication Year: 2005, Page(s): c3
    Request permission for commercial reuse | PDF file iconPDF (34 KB)
    Freely Available from IEEE
  • IEEE Transactions on Evolutionary Computation Information for authors

    Publication Year: 2005, Page(s): c4
    Request permission for commercial reuse | PDF file iconPDF (31 KB)
    Freely Available from IEEE

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