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IEEE Transactions on Evolutionary Computation

Issue 1 • Date Feb. 2010

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Displaying Results 1 - 15 of 15
  • 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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  • Analysis of Computational Time of Simple Estimation of Distribution Algorithms

    Publication Year: 2010, Page(s):1 - 22
    Cited by:  Papers (27)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (329 KB) | HTML iconHTML

    Estimation of distribution algorithms (EDAs) are widely used in stochastic optimization. Impressive experimental results have been reported in the literature. However, little work has been done on analyzing the computation time of EDAs in relation to the problem size. It is still unclear how well EDAs (with a finite population size larger than two) will scale up when the dimension of the optimizat... View full abstract»

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  • Interaction of Culture-Based Learning and Cooperative Co-Evolution and its Application to Automatic Behavior-Based System Design

    Publication Year: 2010, Page(s):23 - 57
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1388 KB) | HTML iconHTML

    Designing an intelligent situated agent is a difficult task because the designer must see the problem from the agent's viewpoint, considering all its sensors, actuators, and computation systems. In this paper, we introduce a bio-inspired hybridization of reinforcement learning, cooperative co-evolution, and a cultural-inspired memetic algorithm for the automatic development of behavior-based agent... View full abstract»

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  • On Set-Based Multiobjective Optimization

    Publication Year: 2010, Page(s):58 - 79
    Cited by:  Papers (44)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1080 KB) | HTML iconHTML

    Assuming that evolutionary multiobjective optimization (EMO) mainly deals with set problems, one can identify three core questions in this area of research: 1) how to formalize what type of Pareto set approximation is sought; 2) how to use this information within an algorithm to efficiently search for a good Pareto set approximation; and 3) how to compare the Pareto set approximations generated by... View full abstract»

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  • Bi-Objective Multipopulation Genetic Algorithm for Multimodal Function Optimization

    Publication Year: 2010, Page(s):80 - 102
    Cited by:  Papers (24)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1027 KB) | HTML iconHTML

    This paper describes the latest version of a bi-objective multipopulation genetic algorithm (BMPGA) aiming to locate all global and local optima on a real-valued differentiable multimodal landscape. The performance of BMPGA is compared against four multimodal GAs on five multimodal functions. BMPGA is distinguished by its use of two separate but complementary fitness objectives designed to enhance... View full abstract»

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  • Grouping Genetic Algorithm for the Blockmodel Problem

    Publication Year: 2010, Page(s):103 - 111
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (236 KB) | HTML iconHTML

    Many areas of research examine the relationships between objects. A subset of these research areas focuses on methods for creating groups whose members are similar based on some specific attribute(s). The blockmodel problem has as its objective to group objects in order to obtain a small number of large groups of similar nodes. In this paper, a grouping genetic algorithm (GGA) is applied to the bl... View full abstract»

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  • HCS: A New Local Search Strategy for Memetic Multiobjective Evolutionary Algorithms

    Publication Year: 2010, Page(s):112 - 132
    Cited by:  Papers (67)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (888 KB) | HTML iconHTML

    In this paper, we propose and investigate a new local search strategy for multiobjective memetic algorithms. More precisely, we suggest a novel iterative search procedure, known as the Hill Climber with Sidestep (HCS), which is designed for the treatment of multiobjective optimization problems, and show further two possible ways to integrate the HCS into a given evolutionary strategy leading to ne... View full abstract»

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  • Customizable FPGA IP Core Implementation of a General-Purpose Genetic Algorithm Engine

    Publication Year: 2010, Page(s):133 - 149
    Cited by:  Papers (30)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1222 KB) | HTML iconHTML

    Hardware implementation of genetic algorithms (GAs) is gaining importance because of their proven effectiveness as optimization engines for real-time applications (e.g., evolvable hardware). Earlier hardware implementations suffer from major drawbacks such as absence of GA parameter programmability, rigid predefined system architecture, and lack of support for multiple fitness functions. In this p... View full abstract»

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  • Niching Without Niching Parameters: Particle Swarm Optimization Using a Ring Topology

    Publication Year: 2010, Page(s):150 - 169
    Cited by:  Papers (59)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1413 KB) | HTML iconHTML

    Niching is an important technique for multimodal optimization. Most existing niching methods require specification of certain niching parameters in order to perform well. These niching parameters, often used to inform a niching algorithm how far apart between two closest optima or the number of optima in the search space, are typically difficult to set as they are problem dependent. This paper des... View full abstract»

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  • Special issue on Evolving developmental Systems

    Publication Year: 2010, Page(s): 170
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  • Special issue on Advances in Memetic Computation

    Publication Year: 2010, Page(s): 171
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  • Why we joined ... [advertisement]

    Publication Year: 2010, Page(s): 172
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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.
 

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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