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Evolutionary Computation, IEEE Transactions on

Issue 4 • Date Aug. 2008

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Displaying Results 1 - 15 of 15
  • Table of contents

    Publication Year: 2008, Page(s): C1
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  • IEEE Transactions on Evolutionary Computation publication information

    Publication Year: 2008, Page(s): C2
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  • The Automatic Acquisition, Evolution and Reuse of Modules in Cartesian Genetic Programming

    Publication Year: 2008, Page(s):397 - 417
    Cited by:  Papers (25)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (909 KB) | HTML iconHTML

    This paper presents a generalization of the graph- based genetic programming (GP) technique known as Cartesian genetic programming (CGP). We have extended CGP by utilizing automatic module acquisition, evolution, and reuse. To benchmark the new technique, we have tested it on: various digital circuit problems, two symbolic regression problems, the lawnmower problem, and the hierarchical if-and-onl... View full abstract»

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  • Protein Folding in Simplified Models With Estimation of Distribution Algorithms

    Publication Year: 2008, Page(s):418 - 438
    Cited by:  Papers (39)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (996 KB) | HTML iconHTML

    Simplified lattice models have played an important role in protein structure prediction and protein folding problems. These models can be useful for an initial approximation of the protein structure, and for the investigation of the dynamics that govern the protein folding process. Estimation of distribution algorithms (EDAs) are efficient evolutionary algorithms that can learn and exploit the sea... View full abstract»

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  • AbYSS: Adapting Scatter Search to Multiobjective Optimization

    Publication Year: 2008, Page(s):439 - 457
    Cited by:  Papers (45)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2825 KB) | HTML iconHTML

    We propose the use of a new algorithm to solve multiobjective optimization problems. Our proposal adapts the well-known scatter search template for single-objective optimization to the multiobjective domain. The result is a hybrid metaheuristic algorithm called Archive-Based hYbrid Scatter Search (AbYSS), which follows the scatter search structure but uses mutation and crossover operators f... View full abstract»

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  • Training Distributed GP Ensemble With a Selective Algorithm Based on Clustering and Pruning for Pattern Classification

    Publication Year: 2008, Page(s):458 - 468
    Cited by:  Papers (4)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (734 KB) | HTML iconHTML

    A boosting algorithm based on cellular genetic programming (GP) to build an ensemble of predictors is proposed. The method evolves a population of trees for a fixed number of rounds and, after each round, it chooses the predictors to include in the ensemble by applying a clustering algorithm to the population of classifiers. Clustering the population allows the selection of the most diverse and fi... View full abstract»

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  • Genetic Programming Approaches for Solving Elliptic Partial Differential Equations

    Publication Year: 2008, Page(s):469 - 478
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (650 KB) | HTML iconHTML

    In this paper, we propose a technique based on genetic programming (GP) for meshfree solution of elliptic partial differential equations. We employ the least-squares collocation principle to define an appropriate objective function, which is optimized using GP. Two approaches are presented for the repair of the symbolic expression for the field variables evolved by the GP algorithm to ensure that ... View full abstract»

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  • Measuring Generalization Performance in Coevolutionary Learning

    Publication Year: 2008, Page(s):479 - 505
    Cited by:  Papers (17)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1452 KB) | HTML iconHTML

    Coevolutionary learning involves a training process where training samples are instances of solutions that interact strategically to guide the evolutionary (learning) process. One main research issue is with the generalization performance, i.e., the search for solutions (e.g., input-output mappings) that best predict the required output for any new input that has not been seen during the evolution... View full abstract»

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  • Crossover-Based Tree Distance in Genetic Programming

    Publication Year: 2008, Page(s):506 - 524
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2115 KB) | HTML iconHTML

    In evolutionary algorithms, distance metrics between solutions are often useful for many aspects of guiding and understanding the search process. A good distance measure should reflect the capability of the search: if two solutions are found to be close in distance, or similarity, they should also be close in the search algorithm sense, i.e., the variation operator used to traverse the search spac... View full abstract»

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  • IEEE Symposium on Computational Intelligence and Games

    Publication Year: 2008, Page(s): 525
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  • IEEE Congress on Evolutionary Computation

    Publication Year: 2008, Page(s): 526
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  • Scitopia.org [advertisement]

    Publication Year: 2008, Page(s): 527
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  • Introducing ieee.tv [advertisement]

    Publication Year: 2008, Page(s): 528
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2008, Page(s): C3
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  • IEEE Transactions on Evolutionary Computation Information for authors

    Publication Year: 2008, 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