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Evolutionary Computation, IEEE Transactions on

Issue 6 • Date Dec. 2010

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  • Table of contents

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

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

    Page(s): 821 - 841
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 also needed for the purpose of normalizing different objectives, so as to facilitate a comparison and agglomeration of the objectives. However, the task of estimating the nadir objective vector necessitates information about the complete Pareto-optimal front and has been reported to be a difficult task, and importantly an unsolved and open research issue. In this paper, we propose certain modifications to an existing evolutionary multiobjective optimization procedure to focus its search toward the extreme objective values and combine it with a reference-point based local search approach to constitute a couple of hybrid procedures for a reliable estimation of the nadir objective vector. With up to 20-objective optimization test problems and on a three-objective engineering design optimization problem, one of the proposed procedures is found to be capable of finding the nadir objective vector reliably. The study clearly shows the significance of an evolutionary computing based search procedure in assisting to solve an age-old important task in the field of multiobjective optimization. View full abstract»

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  • Multimodal Optimization by Means of a Topological Species Conservation Algorithm

    Page(s): 842 - 864
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 local individuals (as in the species conserving genetic algorithm) with a topological subpopulations separation (as in the multinational genetic algorithm) instead of the common but problematic radius-triggered manner. A special treatment for offspring integration, a more rigorous control on the allowed number and uniqueness of the resulting seeds, and a more efficient fitness evaluations budget management further augment a previously suggested naïve combination of the two algorithms. Experiments have been performed on a series of benchmark test functions, including a problem from engineering design. Comparison is primarily conducted to show the significant performance difference to the naïve combination; also the related radius-dependent conserving algorithm is subsequently addressed. Additionally, three more multimodal evolutionary methods, being either conceptually close, competitive as radius-based strategies, or recent state-of-the-art are also taken into account. We detect a clear advantage of three of the six algorithms that, in the case of our method, probably comes from the proper topological separation into subpopulations according to the existing attraction basins, independent of their locations in the function landscape. Additionally, an investigation of the parameter independence of the method as compared to the radius-compelled algorithms is systematically accomplished. View full abstract»

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  • A Dual-Population Genetic Algorithm for Adaptive Diversity Control

    Page(s): 865 - 884
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 population. While most MPGAs use migration as a means of information exchange between different populations, DPGA uses crossbreeding because the two populations have entirely different fitness functions. The reserve population cannot provide useful diversity to the main population unless the two maintain an appropriate distance. Therefore, DPGA adjusts the distance dynamically to achieve an appropriate balance between exploration and exploitation. The experimental results on various classes of problems using binary, real-valued, and order-based representations show that DPGA quite often outperforms not only the standard GAs but also other GAs having additional mechanisms of diversity preservation. View full abstract»

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

    Page(s): 885 - 899
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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-environment interactions can be experimentally manipulated. In this paper, we study the mechanisms of tactile perception in a task in which a neuro-controlled anthropomorphic robotic arm, equipped with coarse-grained tactile sensors, is required to perceptually categorize spherical and ellipsoid objects. We show that best individuals, synthesized by artificial evolution techniques, develop a close to optimal ability to discriminate the shape of the objects as well as an ability to generalize their skill in new circumstances. The results show that the agents solve the categorization task in an effective and robust way by self-selecting the required information through action and by integrating experienced sensory-motor states over time. View full abstract»

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

    Page(s): 900 - 912
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 illustrate quantitative correspondence between the prescribed and the computed robustness. View full abstract»

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

    Page(s): 913 - 941
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 classification tasks, in order to provide a state of the art in this field. This paper has a double aim: to present a taxonomy of the genetics-based machine learning approaches for rule induction, and to develop an empirical analysis both for standard classification and for classification with imbalanced data sets. We also include a comparative study of the genetics-based machine learning (GBML) methods with some classical non-evolutionary algorithms, in order to observe the suitability and high potential of the search performed by evolutionary algorithms and the behavior of the GBML algorithms in contrast to the classical approaches, in terms of classification accuracy. View full abstract»

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

    Page(s): 942 - 958
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 computation to search a space of solutions, we employ it to search a space of heuristics for the problem. A key motivation is to investigate methods to automate the heuristic design process. It has been stated in the literature that humans are very good at identifying good building blocks for solution methods. However, the task of intelligently searching through all of the potential combinations of these components is better suited to a computer. With such tools at their disposal, heuristic designers are then free to commit more of their time to the creative process of determining good components, while the computer takes on some of the design process by intelligently combining these components. This paper shows that a GP hyper-heuristic can be employed to automatically generate human competitive heuristics in a very-well studied problem domain. View full abstract»

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

    Page(s): 959 - 974
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 algorithm employs a hierarchical clustering method to locate and track multiple peaks. A fast local search method is also introduced to search optimal solutions in a promising subregion found by the clustering method. Experimental study is conducted based on the moving peaks benchmark to test the performance of the clustering PSO in comparison with several state-of-the-art algorithms from the literature. The experimental results show the efficiency of the clustering PSO for locating and tracking multiple optima in dynamic environments in comparison with other particle swarm optimization models based on the multiswarm method. View full abstract»

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

    Page(s): 975 - 984
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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 the effectiveness of the method, it has been applied to the resource-constrained project scheduling problem. The computational experiments show that the application of the method to this problem is promising. View full abstract»

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

    Page(s): 985 - 998
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | 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. This is because such a crossover operator, which is called geometric crossover, always generates an offspring in the segment between its two parents under the Hamming distance in the genotype space. That is, the sum of the distances from the generated offspring to its two parents is always equal to the distance between the two parents. In this paper, we first propose a non-geometric binary crossover operator to generate an offspring outside the segment between its two parents. Next, we show some properties of our crossover operator. Then we examine its effects on the behavior of EMO algorithms through computational experiments on knapsack problems with two, four, and six objectives. Experimental results show that our crossover operator can increase the spread of solutions along the Pareto front in EMO algorithms without severely degrading their convergence property. As a result, our crossover operator improves some overall performance measures such as the hypervolume. View full abstract»

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  • Acknowledgment to Reviewers

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

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

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

    Page(s): 1005 - 1012
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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

The IEEE Transactions on Evolutionary Computation publishes high-quality technical papers in the application, design, and theory of evolutionary computation: Readers are encouraged to submit papers that disclose significant technical and practical knowledge, exploratory developments and applications of evolutionary computation. Emphasis is given to engineering systems and scientific applications. The Transactions also contains a letters section, which includes information of current interest as well as comments and rebuttals submitted in connection with published papers.  Representative applications areas include the following aspects of evolutionary computation: 1. Evolutionary optimization particularly with constraints; 2. Machine learning; 3. Intelligent systems design; 4. Image processing. machine vision; 5. Pattern recognition; 6. Evolutionary neurocomputing; 7. Evolutionary fuzzy systems; 8. Applications in biomedicine and biochemistry; 9. Robotics and control; 10. Mathematical modeling; 11. Civil, chemical, aeronautical, and industrial engineering applications.

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Meet Our Editors

Editor-in-Chief
Garrison W. Greenwood, Ph.D. P.E
Portland State University