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IEEE Transactions on Evolutionary Computation

Issue 1 • Date Feb 2002

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Displaying Results 1 - 7 of 7
  • Graph-based evolutionary design of arithmetic circuits

    Publication Year: 2002, Page(s):86 - 100
    Cited by:  Papers (11)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (411 KB) | HTML iconHTML

    We present an efficient graph-based evolutionary optimization technique, called evolutionary graph generation (EGG), and the proposed approach is applied to the design of combinational and sequential arithmetic circuits based on parallel counter-tree architecture. The fundamental idea of EGG is to employ general circuit graphs as individuals and manipulate the circuit graphs directly using new evo... View full abstract»

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  • Local performance of the (1 + 1)-ES in a noisy environment

    Publication Year: 2002, Page(s):30 - 41
    Cited by:  Papers (19)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (250 KB) | HTML iconHTML

    While noise is a phenomenon present in many real world optimization problems, the understanding of its potential effects on the performance of evolutionary algorithms is still incomplete. This paper investigates the effects of fitness proportionate Gaussian noise for a (1 + 1)-ES with isotropic normal mutations on the quadratic sphere in the limit of infinite search-space dimensionality. It is dem... View full abstract»

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  • Effects of phenotypic redundancy in structure optimization

    Publication Year: 2002, Page(s):74 - 85
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (221 KB) | HTML iconHTML

    Concepts from graph theory and molecular evolution are proposed for analyzing the redundancy in the genotype-phenotype mapping in structure optimization stemming from graph isomorphism. Evolutionary topology optimization of neural networks serves as an example. By means of analytical and random-walk methods, it is shown that rare and frequent structures influence the search process: operators that... View full abstract»

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  • A computational reinforced learning scheme to blind image deconvolution

    Publication Year: 2002, Page(s):2 - 15
    Cited by:  Papers (14)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (298 KB) | HTML iconHTML

    This paper presents a new approach to adaptive blind image deconvolution based on computational reinforced learning in an attractor-embedded solution space. The new technique develops an evolutionary strategy that generates the improved blur and image populations progressively. A dynamic attractor space is constructed by integrating the knowledge domain of the blur structures into the algorithm. T... View full abstract»

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  • Preferences and their application in evolutionary multiobjective optimization

    Publication Year: 2002, Page(s):42 - 57
    Cited by:  Papers (100)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (314 KB) | HTML iconHTML

    The paper describes a new preference method and its use in multiobjective optimization. These preferences are developed with a goal to reduce the cognitive overload associated with the relative importance of a certain criterion within a multiobjective design environment involving large numbers of objectives. Their successful integration with several genetic-algorithm-based design search and optimi... View full abstract»

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  • Optimization based on bacterial chemotaxis

    Publication Year: 2002, Page(s):16 - 29
    Cited by:  Papers (119)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (522 KB) | HTML iconHTML

    We present an optimization algorithm based on a model of bacterial chemotaxis. The original biological model is used to formulate a simple optimization algorithm, which is evaluated on a set of standard test problems. Based on this evaluation, several features are added to the basic algorithm using evolutionary concepts in order to obtain an improved optimization strategy, called the bacteria chem... View full abstract»

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  • The particle swarm - explosion, stability, and convergence in a multidimensional complex space

    Publication Year: 2002, Page(s):58 - 73
    Cited by:  Papers (3055)  |  Patents (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (411 KB) | HTML iconHTML

    The particle swarm is an algorithm for finding optimal regions of complex search spaces through the interaction of individuals in a population of particles. This paper analyzes a particle's trajectory as it moves in discrete time (the algebraic view), then progresses to the view of it in continuous time (the analytical view). A five-dimensional depiction is developed, which describes the system co... 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