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Evolutionary Computation, IEEE Transactions on

Issue 3 • Date June 2013

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  • Table of contents

    Publication Year: 2013 , Page(s): C1
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  • IEEE Transactions on Evolutionary Computation publication information

    Publication Year: 2013 , Page(s): C2
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  • Stochastic Diversity Loss and Scalability in Estimation of Distribution Genetic Programming

    Publication Year: 2013 , Page(s): 301 - 320
    Cited by:  Papers (1)
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    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (689 KB) |  | HTML iconHTML  

    In estimation of distribution algorithms (EDAs), probability models hold accumulating evidence on the location of an optimum. Stochastic sampling drift has been heavily researched in EDA optimization but not in EDAs applied to genetic programming (EDA-GP). We show that, for EDA-GPs using probabilistic prototype tree models, stochastic drift in sampling and selection is a serious problem, inhibitin... View full abstract»

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  • A Survey on Multiobjective Evolutionary Algorithms for the Solution of the Portfolio Optimization Problem and Other Finance and Economics Applications

    Publication Year: 2013 , Page(s): 321 - 344
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (681 KB) |  | HTML iconHTML  

    The coinciding development of multiobjective evolutionary algorithms (MOEAs) and the emergence of complex problem formulation in the finance and economics areas has led to a mutual interest from both research communities. Since the 1990s, an increasing number of works have thus proposed the application of MOEAs to solve complex financial and economic problems, involving multiple objectives. This p... View full abstract»

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  • A Note on Generalization Loss When Evolving Adaptive Pattern Recognition Systems

    Publication Year: 2013 , Page(s): 345 - 352
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1276 KB) |  | HTML iconHTML  

    Evolutionary computing provides powerful methods for designing pattern recognition systems. This design process is typically based on finite sample data and therefore bears the risk of overfitting. This paper aims at raising the awareness of various types of overfitting and at providing guidelines for how to deal with them. We restrict our considerations to the predominant scenario in which fitnes... View full abstract»

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  • A Novel Genetic Programming Approach for Frequency-Dependent Modeling

    Publication Year: 2013 , Page(s): 353 - 367
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (12541 KB) |  | HTML iconHTML  

    Frequency-dependent modeling of devices and systems is a common practice in several fields, such as power systems, microwave systems, and electronics systems. The modeling process usually involves converting the tabulated frequency-response data into a compact equivalent circuit model. The main drawback of the currently existing methods such as vector fitting is that the obtained model is often no... View full abstract»

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  • Evolving Diverse Ensembles Using Genetic Programming for Classification With Unbalanced Data

    Publication Year: 2013 , Page(s): 368 - 386
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (4136 KB) |  | HTML iconHTML  

    In classification, machine learning algorithms can suffer a performance bias when data sets are unbalanced. Data sets are unbalanced when at least one class is represented by only a small number of training examples (called the minority class), while the other class(es) make up the majority. In this scenario, classifiers can have good accuracy on the majority class, but very poor accuracy on the m... View full abstract»

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  • A Distance-Based Locally Informed Particle Swarm Model for Multimodal Optimization

    Publication Year: 2013 , Page(s): 387 - 402
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (6363 KB) |  | HTML iconHTML  

    Multimodal optimization amounts to finding multiple global and local optima (as opposed to a single solution) of a function, so that the user can have a better knowledge about different optimal solutions in the search space and when needed, the current solution may be switched to a more suitable one while still maintaining the optimal system performance. Niching particle swarm optimizers (PSOs) ha... View full abstract»

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  • Genetic Variation and the Evolution of Consensus in Digital Organisms

    Publication Year: 2013 , Page(s): 403 - 417
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (6301 KB) |  | HTML iconHTML  

    In this paper, we describe a study of the evolution of consensus, a cooperative behavior in which members in both homogeneous and heterogeneous groups, must agree on information sensed in their environment. We conducted the study using digital evolution, a form of evolutionary computation where a population of computer programs (digital organisms) exists in a user-defined computational environment... View full abstract»

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  • A New Method for Lower Bounds on the Running Time of Evolutionary Algorithms

    Publication Year: 2013 , Page(s): 418 - 435
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (967 KB) |  | HTML iconHTML  

    In this paper a new method for proving lower bounds on the expected running time of evolutionary algorithms (EAs) is presented. It is based on fitness-level partitions and an additional condition on transition probabilities between fitness levels. The method is versatile, intuitive, elegant, and very powerful. It yields exact or near-exact lower bounds for LO, OneMax, long View full abstract»

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  • A New Particle Swarm Optimization Method Enhanced With a Periodic Mutation Strategy and Neural Networks

    Publication Year: 2013 , Page(s): 436 - 452
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (9536 KB) |  | HTML iconHTML  

    Particle swarm optimization (PSO), a relatively new population-based intelligence algorithm, exhibits good performance on optimization problems. However, during the optimization process, the particles become more and more similar, and gather into the neighborhood of the best particle in the swarm, which makes the swarm prematurely converged most likely around the local solution. A new optimization... View full abstract»

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  • Special Issue on Theoretical Foundations of Evolutionary Computation

    Publication Year: 2013 , Page(s): 453
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  • Together, we are advancing technology

    Publication Year: 2013 , Page(s): 454
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  • IEEE Xplore Digital Library

    Publication Year: 2013 , Page(s): 455
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  • Quality without compromise

    Publication Year: 2013 , Page(s): 456
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2013 , Page(s): C3
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  • IEEE Transactions on Evolutionary Computation information for authors

    Publication Year: 2013 , 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