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IEEE Transactions on Evolutionary Computation

Issue 6 • Dec. 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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  • Toward an Estimation of Nadir Objective Vector Using a Hybrid of Evolutionary and Local Search Approaches

    Publication Year: 2010, Page(s):821 - 841
    Cited by:  Papers (38)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (908 KB) | HTML iconHTML

    A nadir objective vector is constructed from the worst Pareto-optimal objective values in a multiobjective optimization problem and is an important entity to compute because of its significance in estimating the range of objective values in the Pareto-optimal front and also in executing a number of interactive multiobjective optimization techniques. Along with the ideal objective vector, it is als... View full abstract»

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  • Multimodal Optimization by Means of a Topological Species Conservation Algorithm

    Publication Year: 2010, Page(s):842 - 864
    Cited by:  Papers (40)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (792 KB) | HTML iconHTML

    Any evolutionary technique for multimodal optimization must answer two crucial questions in order to guarantee some success on a given task: How to most unboundedly distinguish between the different attraction basins and how to most accurately safeguard the consequently discovered solutions. This paper thus aims to present a novel technique that integrates the conservation of the best successive l... View full abstract»

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  • A Dual-Population Genetic Algorithm for Adaptive Diversity Control

    Publication Year: 2010, Page(s):865 - 884
    Cited by:  Papers (28)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1179 KB) | HTML iconHTML

    A variety of previous works exist on maintaining population diversity of genetic algorithms (GAs). Dual-population GA (DPGA) is a type of multipopulation GA (MPGA) that uses an additional population as a reservoir of diversity. The main population is similar to that of an ordinary GA and evolves to find good solutions. The reserve population evolves to maintain and provide diversity to the main po... View full abstract»

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  • Active Categorical Perception of Object Shapes in a Simulated Anthropomorphic Robotic Arm

    Publication Year: 2010, Page(s):885 - 899
    Cited by:  Papers (14)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (970 KB) | HTML iconHTML

    Active perception refers to a theoretical approach to the study of perception grounded on the idea that perceiving is a way of acting, rather than a process whereby the brain constructs an internal representation of the world. The operational principles of active perception can be effectively tested by building robot-based models in which the relationship between perceptual categories and the body... View full abstract»

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  • An Evolutionary Computing Approach to Robust Design in the Presence of Uncertainties

    Publication Year: 2010, Page(s):900 - 912
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (627 KB) | HTML iconHTML

    This paper sets forth a new approach to robust evolutionary computing. In particular, the proposed approach allows users to specify the probability of success in meeting design specifications in the presence of uncertainties. Three benchmark problems have been considered to demonstrate the proposed approach. In addition, a robust electromagnet design example is also considered. The results illustr... View full abstract»

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  • Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy, and Comparative Study

    Publication Year: 2010, Page(s):913 - 941
    Cited by:  Papers (55)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (979 KB) | HTML iconHTML

    The classification problem can be addressed by numerous techniques and algorithms which belong to different paradigms of machine learning. In this paper, we are interested in evolutionary algorithms, the so-called genetics-based machine learning algorithms. In particular, we will focus on evolutionary approaches that evolve a set of rules, i.e., evolutionary rule-based systems, applied to classifi... View full abstract»

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  • A Genetic Programming Hyper-Heuristic Approach for Evolving 2-D Strip Packing Heuristics

    Publication Year: 2010, Page(s):942 - 958
    Cited by:  Papers (38)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (663 KB) | HTML iconHTML

    We present a genetic programming (GP) system to evolve reusable heuristics for the 2-D strip packing problem. The evolved heuristics are constructive, and decide both which piece to pack next and where to place that piece, given the current partial solution. This paper contributes to a growing research area that represents a paradigm shift in search methodologies. Instead of using evolutionary com... View full abstract»

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  • A Clustering Particle Swarm Optimizer for Locating and Tracking Multiple Optima in Dynamic Environments

    Publication Year: 2010, Page(s):959 - 974
    Cited by:  Papers (110)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (366 KB) | HTML iconHTML

    In the real world, many optimization problems are dynamic. This requires an optimization algorithm to not only find the global optimal solution under a specific environment but also to track the trajectory of the changing optima over dynamic environments. To address this requirement, this paper investigates a clustering particle swarm optimizer (PSO) for dynamic optimization problems. This algorit... View full abstract»

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  • An Accelerating Two-Layer Anchor Search With Application to the Resource-Constrained Project Scheduling Problem

    Publication Year: 2010, Page(s):975 - 984
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (455 KB) | HTML iconHTML

    This paper presents a search method that combines elements from evolutionary and local search paradigms by the systematic use of crossover operations, generally used as structured exchange of genes between a series of solutions in genetic algorithms. Crossover operations here are particularly utilized as a systematic means to generate several possible solutions from two superior solutions. To test... View full abstract»

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  • Diversity Improvement by Non-Geometric Binary Crossover in Evolutionary Multiobjective Optimization

    Publication Year: 2010, Page(s):985 - 998
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1179 KB) | HTML iconHTML

    In the design of evolutionary multiobjective optimization (EMO) algorithms, it is important to strike a balance between diversity and convergence. Traditional mask-based crossover operators for binary strings (e.g., one-point, two-point, and uniform) tend to decrease the spread of solutions along the Pareto front in EMO algorithms while they improve the convergence to part of the Pareto front. Thi... View full abstract»

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  • Acknowledgment to Reviewers

    Publication Year: 2010, Page(s):999 - 1002
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  • The big EC 2011 IEEE congress on evolutionary computation

    Publication Year: 2010, Page(s): 1003
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  • Explore IEL IEEE's most comprehensive resource [advertisement]

    Publication Year: 2010, Page(s): 1004
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  • 2010 Index IEEE Transactions on Evolutionary Computation Vol. 14

    Publication Year: 2010, Page(s):1005 - 1012
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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