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Evolutionary Computation, IEEE Transactions on

Issue 5 • Date Oct. 2012

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Displaying Results 1 - 14 of 14
  • Table of contents

    Page(s): C1
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  • IEEE Transactions on Evolutionary Computation publication information

    Page(s): C2
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  • Differential Evolution With Neighborhood Mutation for Multimodal Optimization

    Page(s): 601 - 614
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3244 KB)  

    In this paper, a neighborhood mutation strategy is proposed and integrated with various niching differential evolution (DE) algorithms to solve multimodal optimization problems. Although variants of DE are highly effective in locating a single global optimum, no DE variant performs competitively when solving multi-optima problems. In the proposed neighborhood based differential evolution, the mutation is performed within each Euclidean neighborhood. The neighborhood mutation is able to maintain the multiple optima found during the evolution and evolve toward the respective global/local optimum. To test the performance of the proposed neighborhood mutation DE, a total of 29 problem instances are used. The proposed algorithms are compared with a number of state-of-the-art multimodal optimization approaches and the experimental results suggest that although the idea of neighborhood mutation is simple, it is able to provide better and more consistent performance over the state-of-the-art multimodal algorithms. In addition, a comparative survey on niching algorithms and their applications are also presented. View full abstract»

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  • Progressive Alignment Method Using Genetic Algorithm for Multiple Sequence Alignment

    Page(s): 615 - 631
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (8623 KB) |  | HTML iconHTML  

    In this paper, we have proposed a progressive alignment method using a genetic algorithm for multiple sequence alignment, named GAPAM. We have introduced two new mechanisms to generate an initial population: the first mechanism is to generate guide trees with randomly selected sequences and the second is shuffling the sequences inside such trees. Two different genetic operators have been implemented with GAPAM. To test the performance of our algorithm, we have compared it with existing well-known methods, such as PRRP, CLUSTALX, DIALIGN, HMMT, SB_PIMA, ML_PIMA, MULTALIGN, and PILEUP8, and also other methods, based on genetic algorithms (GA), such as SAGA, MSA-GA, and RBT-GA, by solving a number of benchmark datasets from BAliBase 2.0. To make a fairer comparison with the GA based algorithms such as MSA-GA and RBT-GA, we have performed further experiments covering all the datasets reported by those two algorithms. The experimental results showed that GAPAM achieved better solutions than the others for most of the cases, and also revealed that the overall performance of the proposed method outperformed the other methods mentioned above. View full abstract»

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  • Approximating the Genetic Diversity of Populations in the Quasi-Equilibrium State

    Page(s): 632 - 644
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4987 KB) |  | HTML iconHTML  

    This paper analyzes an evolutionary algorithm in the quasi-equilibrium state, i.e., when the population of chromosomes fluctuates around a single peak of the fitness function. The analysis is aimed at approximating the genetic variance of the population when chromosomes are real-valued. The infinite population model is considered which allows the quasi-equilibrium state to be defined as the state when the density of chromosomes contained by the population remains unchanged over consecutive generations. This paper provides formulas for genetic diversity in the quasi-equilibrium state for fitness proportionate, tournament, and truncation selection types, with and without elitism, with Gaussian mutation, and with and without arithmetic crossover. The formulas are experimentally validated. View full abstract»

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  • A Filter Approach to Multiple Feature Construction for Symbolic Learning Classifiers Using Genetic Programming

    Page(s): 645 - 661
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6230 KB) |  | HTML iconHTML  

    Feature construction is an effort to transform the input space of classification problems in order to improve the classification performance. Feature construction is particularly important for classifier inducers that cannot transform their input space intrinsically. This paper proposes GPMFC, a multiple-feature construction system for classification problems using genetic programming (GP). This paper takes a nonwrapper approach by introducing a filter-based measure of goodness for constructed features. The constructed, high-level features are functions of original input features. These functions are evolved by GP using an entropy-based fitness function that maximizes the purity of class intervals. A decomposable objective function is proposed so that the system is able to construct multiple high-level features for each problem. The constructed features are used to transform the original input space to a new space with better separability. Extensive experiments are conducted on a number of benchmark problems and symbolic learning classifiers. The results show that, in most cases, the new approach is highly effective in increasing the classification performance in rule-based and decision tree classifiers. The constructed features help improve the learning performance of symbolic learners. The constructed features, however, may lack intelligibility. View full abstract»

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  • Multiobjective Evolutionary Algorithms in Aeronautical and Aerospace Engineering

    Page(s): 662 - 694
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1488 KB) |  | HTML iconHTML  

    Nowadays, the solution of multiobjective optimization problems in aeronautical and aerospace engineering has become a standard practice. These two fields offer highly complex search spaces with different sources of difficulty, which are amenable to the use of alternative search techniques such as metaheuristics, since they require little domain information to operate. From the several metaheuristics available, multiobjective evolutionary algorithms (MOEAs) have become particularly popular, mainly because of their availability, ease of use, and flexibility. This paper presents a taxonomy and a comprehensive review of applications of MOEAs in aeronautical and aerospace design problems. The review includes both the characteristics of the specific MOEA adopted in each case, as well as the features of the problems being solved with them. The advantages and disadvantages of each type of approach are also briefly addressed. We also provide a set of general guidelines for using and designing MOEAs for aeronautical and aerospace engineering problems. In the final part of the paper, we provide some potential paths for future research, which we consider promising within this area. View full abstract»

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  • Review and Study of Genotypic Diversity Measures for Real-Coded Representations

    Page(s): 695 - 710
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (10475 KB) |  | HTML iconHTML  

    The exploration/exploitation balance is a major concern in the control of evolutionary algorithms (EAs) performance. Exploration is associated with the distribution of individuals on a landscape, and can be estimated by a genotypic diversity measure (GDM). In contrast, exploitation is related to individual responses, which can be described with a phenotypic diversity measure. Many diversity measures have been proposed in the literature without a comprehensive study of their differences. This paper looks at surveys of GDMs published over the years for real-coded representations, and compares them based on a new benchmark, one that allows a better description of their behavior. The results demonstrate that none of the available GDMs is able to reflect the true diversity of all search processes. Nonetheless, the normalized pairwise diversity measurement proves to be the best genotypic diversity measurement for standard EAs, as it shows nondominated behavior with respect to the desired GDM requirements. View full abstract»

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  • Evolution of Plastic Learning in Spiking Networks via Memristive Connections

    Page(s): 711 - 729
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (13206 KB) |  | HTML iconHTML  

    This paper presents a spiking neuroevolutionary system which implements memristors as plastic connections, i.e., whose weights can vary during a trial. The evolutionary design process exploits parameter self-adaptation and variable topologies, allowing the number of neurons, connection weights, and interneural connectivity pattern to emerge. By comparing two phenomenological real-world memristor implementations with networks comprised of: 1) linear resistors, and 2) constant-valued connections, we demonstrate that this approach allows the evolution of networks of appropriate complexity to emerge whilst exploiting the memristive properties of the connections to reduce learning time. We extend this approach to allow for heterogeneous mixtures of memristors within the networks; our approach provides an in-depth analysis of network structure. Our networks are evaluated on simulated robotic navigation tasks; results demonstrate that memristive plasticity enables higher performance than constant-weighted connections in both static and dynamic reward scenarios, and that mixtures of memristive elements provide performance advantages when compared to homogeneous memristive networks. View full abstract»

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  • Memetic Algorithms for De Novo Motif Discovery

    Page(s): 730 - 748
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4814 KB) |  | HTML iconHTML  

    Identifying the unknown transcription factor binding sites (TFBSs) is a fundamental and important component for understanding gene regulation as well as life mechanisms. The corresponding de novo motif discovery problem in bioinformatics is formulated as pattern discovery from strings, where challenges come from both modeling and optimization, because the short TFBSs are weak signals in massive and noisy experimental data. While genetic algorithms have been widely applied to the problem, recent memetic algorithms (MAs) employing local operators demonstrate the superiority in both effectiveness and efficiency. In this paper, we propose and study various MA components including local operators and models for motif discovery, through the newly established MA framework. The demonstrated optimization and modeling capabilities are analyzed in-depth on real datasets and their noisy versions. Selected optimal MAs show significantly improved performance over state-of-the-art methods in extensive tests including the blind test on the eukaryotic benchmark. This paper serves as the first systematic study of MAs on de novo motif discovery, where important issues are highlighted in the analyses of MA design. The comprehensive component categorization and the MA framework provide a useful platform for future MA developments, especially on the newly emerging chromatin immunoprecipitation followed by sequencing data. View full abstract»

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  • Genetic Algorithms for Discovery of Matrix Multiplication Methods

    Page(s): 749 - 751
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    We present a parallel genetic algorithm for finding matrix multiplication algorithms. For 3 3 matrices our genetic algorithm successfully discovered algorithms requiring 23 multiplications, which are equivalent to the currently best known human-developed algorithms. We also studied cases with fewer multiplications and found an approximate solution for 22 multiplications. View full abstract»

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  • Erratum to “On the Design of Constraint Covariance Matrix Self-Adaptation Evolution Strategies Including a Cardinality Constraint” [Aug 12 578-596]

    Page(s): 752
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (94 KB)  

    In the above-named article [ibid., vol 16, no 4, pp. 578-596, Aug. 2012] an error occurred during the print production process that resulted in the incorrect display of Greek characters within many of the figures in the printed publication. However, the electronic PDF version available on IEEEXplore was not affected and all of the articles' figures appear correctly. Please visit http://ieeexplore. ieee.org/xpl/tocresult.jsp?isnumber=6249762&punumber=4235 to access the correct article of record. The affected figures in print were as follows: Fig. 5(a) vertical axis label incomplete, Greek rho missing; Fig. 6(a) vertical axis label incomplete, Greek rho missing; Fig. 9(a) vertical axis label incomplete, Greek rho missing; Fig. 9(c) vertical axis label incomplete, Greek sigma missing; Fig. 10(a) vertical axis label incomplete, Greek rho missing; Fig. 10(b) vertical axis label incomplete, Greek rho missing; Fig. 11(a) vertical axis label incomplete, Greek rho missing; Fig. 11(c) vertical axis label incomplete, Greek sigma missing; Fig. 12(a) vertical axis label incomplete, Greek rho missing; Fig. 12(c) vertical axis label incomplete, Greek sigma missing; Fig. 13(a) vertical axis label incomplete, Greek rho missing; Fig. 13(c) vertical axis label incomplete, Greek sigma missing; Fig. 14(a) and (b) vertical axis labels incomplete, Greek rho overlined (bar) missing; Fig. 17(a) and (b) horizontal axis labels, Greek kappa missing; Fig. 18 horizontal axis label, Greek kappa missing; Fig. 19(b) vertical axis label incomplete, Greek lambda missing; Fig. 21(a) and (b) horizontal axis labels, Greek kappa missing; Fig. 22(b) Delta before "f" and "x" missing; Fig. 24(a) and (b) horizontal axis labels, Greek lambda missing. View full abstract»

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  • IEEE Computational Intelligence Society Information

    Page(s): C3
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  • IEEE Transactions on Evolutionary Computation - information for authors

    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
Garrison W. Greenwood, Ph.D. P.E
Portland State University