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IEEE Transactions on Evolutionary Computation

Issue 5 • Oct. 2011

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Displaying Results 1 - 19 of 19
  • Table of contents

    Publication Year: 2011, Page(s): C1
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  • IEEE Transactions on Evolutionary Computation publication information

    Publication Year: 2011, Page(s): C2
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  • Guest Editorial

    Publication Year: 2011, Page(s):589 - 590
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  • A Multi-Facet Survey on Memetic Computation

    Publication Year: 2011, Page(s):591 - 607
    Cited by:  Papers (188)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (452 KB) | HTML iconHTML

    Memetic computation is a paradigm that uses the notion of meme(s) as units of information encoded in computational representations for the purpose of problem-solving. It covers a plethora of potentially rich meme-inspired computing methodologies, frameworks and operational algorithms including simple hybrids, adaptive hybrids and memetic automaton. In this paper, a comprehensive multi-facet survey... View full abstract»

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  • Analysis on the Collaboration Between Global Search and Local Search in Memetic Computation

    Publication Year: 2011, Page(s):608 - 623
    Cited by:  Papers (20)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (746 KB) | HTML iconHTML

    The synergy between exploration and exploitation has been a prominent issue in optimization. The rise of memetic algorithms, a category of optimization techniques which feature the explicit exploration-exploitation coordination, much accentuates this issue. While memetic algorithms have achieved remarkable success in a wide range of real-world applications, the key to successful exploration-exploi... View full abstract»

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  • A Multilevel Memetic Approach for Improving Graph k-Partitions

    Publication Year: 2011, Page(s):624 - 642
    Cited by:  Papers (27)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1239 KB) | HTML iconHTML

    Graph partitioning is one of the most studied NP-complete problems. Given a graph G=(V, E) , the task is to partition the vertex set V into k disjoint subsets of about the same size, such that the number of edges with endpoints in different subsets is minimized. In this paper, we present a highly effective multilevel memetic algorithm, which integrates a new mult... View full abstract»

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  • DNA Sequence Compression Using Adaptive Particle Swarm Optimization-Based Memetic Algorithm

    Publication Year: 2011, Page(s):643 - 658
    Cited by:  Papers (44)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (838 KB) | HTML iconHTML

    With the rapid development of high-throughput DNA sequencing technologies, the amount of DNA sequence data is accumulating exponentially. The huge influx of data creates new challenges for storage and transmission. This paper proposes a novel adaptive particle swarm optimization-based memetic algorithm (POMA) for DNA sequence compression. POMA is a synergy of comprehensive learning particle swarm ... View full abstract»

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  • A Memetic Algorithm for Global Optimization in Chemical Process Synthesis Problems

    Publication Year: 2011, Page(s):659 - 683
    Cited by:  Papers (21)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1112 KB) | HTML iconHTML

    Design optimization problems in chemical engineering and in many other engineering domains are characterized by the presence of a large number of discrete and continuous decision variables, complex nonlinear models that restrict the search space, nonlinear cost functions, and the presence of many local optima. The classical approach to such problems are mixed integer nonlinear program solvers that... View full abstract»

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  • Optimization Approach for 4-D Natural Landscape Evolution

    Publication Year: 2011, Page(s):684 - 691
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (561 KB) | HTML iconHTML

    This paper presents a unique optimization method developed for landscape evolution problems. An existing hypothesis of the optimal channel network states that fluvial landscape evolution can be characterized as the procedure that follows minimum total energy expenditure. Previous studies have tested this hypothesis by solving an optimization problem, i.e., finding landscapes that satisfy the minim... View full abstract»

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  • Maintaining Healthy Population Diversity Using Adaptive Crossover, Mutation, and Selection

    Publication Year: 2011, Page(s):692 - 714
    Cited by:  Papers (22)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2869 KB) | HTML iconHTML

    This paper presents ACROMUSE, a novel genetic algorithm (GA) which adapts crossover, mutation, and selection parameters. ACROMUSEs objective is to create and maintain a diverse population of highly-fit (healthy) individuals, capable of adapting quickly to fitness landscape change and well-suited to the efficient optimization of multimodal fitness landscapes. A new methodology is introduced for det... View full abstract»

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  • Multi Population Pattern Searching Algorithm: A New Evolutionary Method Based on the Idea of Messy Genetic Algorithm

    Publication Year: 2011, Page(s):715 - 734
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1390 KB) | HTML iconHTML

    One of the main evolutionary algorithms bottlenecks is the significant effectiveness dropdown caused by increasing number of genes necessary for coding the problem solution. In this paper, we present a multi population pattern searching algorithm (MuPPetS), which is supposed to be an answer to situations where long coded individuals are a must. MuPPetS uses some of the messy GA ideas like coding a... View full abstract»

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  • IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

    Publication Year: 2011, Page(s): 735
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  • Special Issue on Understanding Complex Evolutionary Systems

    Publication Year: 2011, Page(s): 736
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  • IEEE Transactions on Neural Networks & Learning Systems

    Publication Year: 2011, Page(s): 737
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  • Explore IEL IEEE's most comprehensive resource [advertisement]

    Publication Year: 2011, Page(s): 738
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    Publication Year: 2011, Page(s): 739
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    Publication Year: 2011, Page(s): 740
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2011, Page(s): C3
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  • IEEE Transactions on Evolutionary Computation Information for authors

    Publication Year: 2011, Page(s): C4
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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.
 

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: kaytan@cityu.edu.hk

Website: http://vlab.ee.nus.edu.sg/~kctan