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Evolutionary Computation, IEEE Transactions on

Issue 5 • Date Oct. 2010

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  • Table of contents

    Publication Year: 2010 , Page(s): C1
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  • IEEE Transactions on Evolutionary Computation publication information

    Publication Year: 2010 , Page(s): C2
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  • Guest Editorial Special Issue on Preference-Based Multiobjective Evolutionary Algorithms

    Publication Year: 2010 , Page(s): 669 - 670
    Cited by:  Papers (3)
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  • Brain–Computer Evolutionary Multiobjective Optimization: A Genetic Algorithm Adapting to the Decision Maker

    Publication Year: 2010 , Page(s): 671 - 687
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1278 KB) |  | HTML iconHTML  

    The centrality of the decision maker (DM) is widely recognized in the multiple criteria decision-making community. This translates into emphasis on seamless human-computer interaction, and adaptation of the solution technique to the knowledge which is progressively acquired from the DM. This paper adopts the methodology of reactive search optimization (RSO) for evolutionary interactive multiobject... View full abstract»

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  • Integration of Preferences in Hypervolume-Based Multiobjective Evolutionary Algorithms by Means of Desirability Functions

    Publication Year: 2010 , Page(s): 688 - 701
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1074 KB) |  | HTML iconHTML  

    In this paper, a concept for efficiently approximating the practically relevant regions of the Pareto front (PF) is introduced. Instead of the original objectives, desirability functions (DFs) of the objectives are optimized, which express the preferences of the decision maker. The original problem formulation and the optimization algorithm do not have to be modified. DFs map an objective to the d... View full abstract»

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  • An Interactive Territory Defining Evolutionary Algorithm: iTDEA

    Publication Year: 2010 , Page(s): 702 - 722
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (923 KB) |  | HTML iconHTML  

    We develop a preference-based multiobjective evolutionary algorithm that interacts with the decision maker (DM) during the course of optimization. We create a territory around each solution where no other solutions are allowed. We define smaller territories around the preferred solutions in order to obtain denser coverage of these regions. At each interaction, the algorithm asks the DM to choose h... View full abstract»

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  • An Interactive Evolutionary Multiobjective Optimization Method Based on Progressively Approximated Value Functions

    Publication Year: 2010 , Page(s): 723 - 739
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1452 KB) |  | HTML iconHTML  

    This paper suggests a preference-based methodology, which is embedded in an evolutionary multiobjective optimization algorithm to lead a decision maker (DM) to the most preferred solution of her or his choice. The progress toward the most preferred solution is made by accepting preference based information progressively from the DM after every few generations of an evolutionary multiobjective opti... View full abstract»

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  • Feasibility Structure Modeling: An Effective Chaperone for Constrained Memetic Algorithms

    Publication Year: 2010 , Page(s): 740 - 758
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (408 KB) |  | HTML iconHTML  

    An important issue in designing memetic algorithms (MAs) is the choice of solutions in the population for local refinements, which becomes particularly crucial when solving computationally expensive problems. With single evaluation of the objective/constraint functions necessitating tremendous computational power and time, it is highly desirable to be able to focus search efforts on the regions wh... View full abstract»

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  • Multiobjective Optimization Applied to the Eradication of Persistent Pathogens

    Publication Year: 2010 , Page(s): 759 - 765
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (379 KB) |  | HTML iconHTML  

    In scenarios such as therapeutic modeling or pest control, one aims to suppress infective agents or maximize crop yields while minimizing the side-effects of interventions, such as cost, environmental impact, and toxicity. Here, we consider the eradication of persistent microbes (e.g., Escherichia coli, multiply resistant Staphylococcus aureus (MRSA-“superbug”), Mycobacterium tubercu... View full abstract»

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  • Hybrid Genetic Algorithm Using a Forward Encoding Scheme for Lifetime Maximization of Wireless Sensor Networks

    Publication Year: 2010 , Page(s): 766 - 781
    Cited by:  Papers (19)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (968 KB) |  | HTML iconHTML  

    Maximizing the lifetime of a sensor network by scheduling operations of sensors is an effective way to construct energy efficient wireless sensor networks. After the random deployment of sensors in the target area, the problem of finding the largest number of disjoint sets of sensors, with every set being able to completely cover the target area, is nondeterministic polynomial-complete. This paper... View full abstract»

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  • Population-Based Algorithm Portfolios for Numerical Optimization

    Publication Year: 2010 , Page(s): 782 - 800
    Cited by:  Papers (28)
    Save to Project icon | Click to expandAbstract | PDF file iconPDF (642 KB) |  | HTML iconHTML  

    In this paper, we consider the scenario that a population-based algorithm is applied to a numerical optimization problem and a solution needs to be presented within a given time budget. Although a wide range of population-based algorithms, such as evolutionary algorithms, particle swarm optimizers, and differential evolution, have been developed and studied under this scenario, the performance of ... View full abstract»

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  • The r-Dominance: A New Dominance Relation for Interactive Evolutionary Multicriteria Decision Making

    Publication Year: 2010 , Page(s): 801 - 818
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1407 KB) |  | HTML iconHTML  

    Evolutionary multiobjective optimization (EMO) methodologies have gained popularity in finding a representative set of Pareto optimal solutions in the past decade and beyond. Several techniques have been proposed in the specialized literature to ensure good convergence and diversity of the obtained solutions. However, in real world applications, the decision maker is not interested in the overall ... View full abstract»

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  • The big EC 2011 IEEE congress on evolutionary computation

    Publication Year: 2010 , Page(s): 819
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  • Have you visited lately? www.ieee.org [advertisement]

    Publication Year: 2010 , Page(s): 820
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2010 , Page(s): C3
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  • IEEE Transactions on Evolutionary Computation Information for authors

    Publication Year: 2010 , 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: eletankc@nus.edu.sg

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