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Evolutionary Computation, IEEE Transactions on

Issue 6 • Date Dec. 2002

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Displaying Results 1 - 11 of 11
  • Editorial: transition to a new editor-in-chief

    Publication Year: 2002 , Page(s): 529 - 530
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    Freely Available from IEEE
  • Editorial: Welcome To The IEEE Neural Networks Society

    Publication Year: 2002 , Page(s): 531 - 532
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    Freely Available from IEEE
  • Acknowledgment to reviewers

    Publication Year: 2002 , Page(s): 610 - 611
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    Freely Available from IEEE
  • Author index

    Publication Year: 2002 , Page(s): 612 - 613
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    Freely Available from IEEE
  • Subject index

    Publication Year: 2002 , Page(s): 613 - 618
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  • Toward minimal restriction of genetic encoding and crossovers for the two-dimensional Euclidean TSP

    Publication Year: 2002 , Page(s): 557 - 565
    Cited by:  Papers (17)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (488 KB) |  | HTML iconHTML  

    In the field of evolutionary algorithms (EAs), many operators have been introduced for the traveling salesman problem (TSP). Most encoding schemes have various restrictions that often result in a loss of information contained in problem instances. We suggest a new chromosomal encoding scheme that pursues minimal information loss and a crossover scheme with minimal restriction for the two-dimension... View full abstract»

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  • A genetic algorithm for shortest path routing problem and the sizing of populations

    Publication Year: 2002 , Page(s): 566 - 579
    Cited by:  Papers (158)  |  Patents (1)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1466 KB) |  | HTML iconHTML  

    This paper presents a genetic algorithmic approach to the shortest path (SP) routing problem. Variable-length chromosomes (strings) and their genes (parameters) have been used for encoding the problem. The crossover operation exchanges partial chromosomes (partial routes) at positionally independent crossing sites and the mutation operation maintains the genetic diversity of the population. The pr... View full abstract»

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  • A (μ + λ) evolutionary algorithm for reconstruction of SPECT data

    Publication Year: 2002 , Page(s): 533 - 541
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1208 KB) |  | HTML iconHTML  

    Algorithms used to reconstruct single photon emission computed tomography (SPECT) data are based on one of two principles: filtered back projection or iterative methods. In this paper, an evolution strategy (ES) was applied to reconstruct transaxial slices of SPECT data. Evolutionary algorithms are stochastic global search methods that have been used successfully for many kinds of optimization pro... View full abstract»

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  • Quantum-inspired evolutionary algorithm for a class of combinatorial optimization

    Publication Year: 2002 , Page(s): 580 - 593
    Cited by:  Papers (314)  |  Patents (1)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1204 KB) |  | HTML iconHTML  

    This paper proposes a novel evolutionary algorithm inspired by quantum computing, called a quantum-inspired evolutionary algorithm (QEA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other evolutionary algorithms, QEA is also characterized by the representation of the individual, evaluation function, and population dynam... View full abstract»

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  • Evolving pattern recognition systems

    Publication Year: 2002 , Page(s): 594 - 609
    Cited by:  Papers (22)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1062 KB) |  | HTML iconHTML  

    A hybrid evolutionary learning algorithm is presented that synthesizes a complete multiclass pattern recognition system. The approach uses a multifaceted representation that evolves layers of processing to perform feature extraction from raw input data, select cooperative sets of feature detectors, and assemble a linear classifier that uses the detectors' responses to label targets. The hybrid alg... View full abstract»

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  • Artificial worlds modeling of human resource management systems

    Publication Year: 2002 , Page(s): 542 - 556
    Cited by:  Papers (2)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (651 KB) |  | HTML iconHTML  

    Effective human resource management facilitates the success of an organization and the progress of a society. We describe an evolutionary computer model that simulates different modes of interaction between people and their environment. A two-level genotype-phenotype structure is used to represent the characteristics of an individual. The environment is modeled as a two-dimensional array of region... 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