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Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on

Issue 5 • Date Sept. 2012

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  • Table of contents

    Publication Year: 2012 , Page(s): C1
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  • IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews publication information

    Publication Year: 2012 , Page(s): C2
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  • Guest Editorial: Special Issue on Engineering Applications of Memetic Computing

    Publication Year: 2012 , Page(s): 609 - 611
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (131 KB)  

    The seven full papers and one technical correspondence in this special issue focus on the engineering applications of memetic computing. View full abstract»

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  • A Conceptual Modeling of Meme Complexes in Stochastic Search

    Publication Year: 2012 , Page(s): 612 - 625
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2211 KB) |  | HTML iconHTML  

    In science, gene provides the instruction for making proteins, while meme is the sociocultural equivalent of a gene containing instructions for carrying out behavior. Taking inspiration from nature, we model the memeplex in search as instructions that specify the coadapted meme complexes of individuals in their lifetime. In particular, this paper presents a study on the conceptual modeling of meme complexes or memeplexes for more effective problem solving in the context of modern stochastic optimization. The memeplex representation, credit assignment criteria for meme coadaptation, and the role of emergent memeplexes in the lifetime learning process of a memetic algorithm in search are presented. A coadapted memetic algorithm that takes the proposed conceptual modeling of memeplexes into actions to solve capacitated vehicle routing problems (CVRPs) of diverse characteristics is then designed. Results showed that adaptive memeplexes provide a means of creating highly robust, self-configuring, and scalable algorithms, thus generating improved or competitive results when benchmarking against several existing adaptive or human-designed state-of-the-art memetic algorithms and metaheuristics, on a plethora of CVRP sets considered. View full abstract»

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  • Quantum Memetic Evolutionary Algorithm-Based Low-Complexity Signal Detection for Underwater Acoustic Sensor Networks

    Publication Year: 2012 , Page(s): 626 - 640
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (669 KB) |  | HTML iconHTML  

    Modern communication engineering has brought forward impractical requirements on powerful computation engines as well as simple implementations. Apparently, the two aspects are contradicted in most realistic applications. Because of the dispersive multipath propagation in underwater acoustic channels, traditional coherent and adaptive receivers are computationally intensive and, hence, inapplicable to the large-scale underwater sensor networks. Inspired by quantum computing and nature intelligence that are incorporated with the concept of culture evolution, in this paper, we suggest a novel quantum memetic algorithm (QMA) built with more qualified problem-solving ability. Instead of classical gene representations, the quantum bit structure is employed by chromosomes to enhance the population diversity of genetic searching. The quantum gate rotating is then explored to update chromosomes in an efficiently parallel way. As a hybridization strategy, quantum-rotation-based local search is integrated in the lifetime learning to further refine individuals' performance and accelerate their convergence toward the global optimality. As a significant real-world application, we develop a noncoherent underwater signal receiver that is based on a QMA framework. From a pattern recognition aspect, the suggested detection scheme includes two sequential phases: Features extraction and pattern classification. Finally, the highly computational optimization problem is elegantly addressed by QMA. Providing favorable robustness to various parameter configurations, QMA can considerably reinforce the search performance and improve the underwater signal detection. It is demonstrated from numerical experiments that QMA is much superior to genetic algorithm (GA) in this high-dimensional optimization. Meanwhile, QMA shows remarkable advantages in search performance, even to the current state-of-the-art quantum-inspired GA and memetic algorithm. View full abstract»

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  • Neighborhood Search-Driven Accelerated Biogeography-Based Optimization for Optimal Load Dispatch

    Publication Year: 2012 , Page(s): 641 - 652
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (733 KB) |  | HTML iconHTML  

    Lack of exploration capability of biogeography-based optimization (BBO) leads to slow convergence. To address this limitation, this paper presents a memetic algorithm (MA), namely, aBBOmDE, which is a new version of BBO to solve both complex and noncomplex economic load dispatch (ELD) problems of thermal plant. In aBBOmDE, the performance of BBO is accelerated by using a modified mutation and clear duplicate operators. Then, modified DE (mDE) is embedded as a neighborhood search operator to improve their fitness after a predefined threshold. mDE is used with mutation operator DE/best/1/bin to explore the search near the best solution. The length of local search is set to achieve a balance between the search capability and the excess computational cost. In aBBOmDE, migration mechanism is kept same as that of BBO to maintain its exploitation ability. Modified operators are utilized to enhance the exploration ability while a neighborhood search operator, further, enhances the search capability of the algorithm. This combination significantly improves the convergence characteristics of the original algorithm. The effectiveness of the proposed algorithm has been verified on five different test systems with varying degree of complexity. The results have been compared with other existing techniques. The results indicate that the proposed approach can efficiently solve practical ELD problems. View full abstract»

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  • Evolutionary Multivalued Decision Diagrams for Obtaining Motion Representation of Humanoid Robots

    Publication Year: 2012 , Page(s): 653 - 663
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (773 KB) |  | HTML iconHTML  

    In this paper, we propose a method, using multivalued decision diagrams (MDDs), to obtain motion representation of humanoid robots. Kanoh et al. have proposed a method, which uses multiterminal binary decision diagrams (MTBDDs), to acquire robot controller. However, nonterminal vertices of MTBDDs can only treat values of 0 or 1; multiple variables are needed to represent a single joint angle. This increases the number of the non-terminal vertices and MTBDDs that represent that the controller becomes complex. Therefore, we consider using the MDD, in which its nonterminal vertices can take on multiple output values. To obtain humanoid robot motion representation, we propose evolutionary MDDs and show experimental results comparing evolutionary MDDs and evolutionary MTBDDs through simulations of acquisition of robot motion in this paper. Moreover, we verify whether the evolution of MDD using a memetic algorithm is effective. View full abstract»

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  • University Course Timetabling Using a Hybrid Harmony Search Metaheuristic Algorithm

    Publication Year: 2012 , Page(s): 664 - 681
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1300 KB) |  | HTML iconHTML  

    University course timetabling problem (UCTP) is considered to be a hard combinatorial optimization problem to assign a set of events to a set of rooms and timeslots. Although several methods have been investigated, due to the nature of UCTP, memetic computing techniques have been more effective. A key feature of memetic computing is the hybridization of a population-based global search and the local improvement. Such hybridization is expected to strike a balance between exploration and exploitation of the search space. In this paper, a memetic computing technique that is designed for UCTP, called the hybrid harmony search algorithm (HHSA), is proposed. In HHSA, the harmony search algorithm (HSA), which is a metaheuristic population-based method, has been hybridized by: 1) hill climbing, to improve local exploitation; and 2) a global-best concept of particle swarm optimization to improve convergence. The results were compared against 27 other methods using the 11 datasets of Socha et al. comprising five small, five medium, and one large datasets. The proposed method achieved the optimal solution for the small dataset with comparable results for the medium datasets. Furthermore, in the most complex and large datasets, the proposed method achieved the best results. View full abstract»

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  • A Hybrid Estimation of Distribution Algorithm with Decomposition for Solving the Multiobjective Multiple Traveling Salesman Problem

    Publication Year: 2012 , Page(s): 682 - 691
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (647 KB) |  | HTML iconHTML  

    Evolutionary multiobjective optimization with decomposition, in which the algorithm is not required to differentiate between the dominated and nondominated solutions, is one of the promising approaches in dealing with multiple conflicting objectives. In this paper, the estimation of distribution algorithm (EDA) is integrated into the decomposition framework. The search behavior of the algorithm is further enhanced by hybridizing local search metaheuristic approaches with the decomposition EDA. Three local search techniques, including hill climbing, simulated annealing, and evolutionary gradient search, are considered. A novel multiobjective formulation of the multiple traveling salesman problem is proposed. The hybrid algorithms are used to solve the formulated problem with different number of objective functions, salesmen, and problem sizes. The effectiveness and efficiency of the algorithms are tested and benchmarked against several state-of-the-art multiobjective evolutionary paradigms. View full abstract»

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  • Improving Grammar Inference by a Memetic Algorithm

    Publication Year: 2012 , Page(s): 692 - 703
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (478 KB) |  | HTML iconHTML  

    A memetic algorithm, a novel approach for solving NP-hard problems, has been applied in this paper for grammatical inference in the field of domain-specific languages (DSLs). DSLs are often designed by domain experts who have no knowledge about the syntax and semantics of programming languages. However, they are able to write sample programs to accomplish their goals and illustrate the features of their language. Grammatical inference is a technique to infer a context-free grammar from a set of positive (and negative) samples. This paper shows that grammatical inference may assist domain experts and software language engineers in developing DSLs by automatically producing a grammar, which describes a set of sample DSL programs. A memetic-algorithm-based tool is developed, which greatly improves results and robustness of the inference process. View full abstract»

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  • Automatic Design of an Indoor User Location Infrastructure Using a Memetic Multiobjective Approach

    Publication Year: 2012 , Page(s): 704 - 709
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (404 KB) |  | HTML iconHTML  

    Services in ambient intelligence (AmI) environments should adapt to contextual information (context-aware) of users and environment in a nonintrusive and natural way. Location-aware, i.e., the user location, is one of the most important pieces in context-aware. According to this premise, a location-based service (LBS) using radio frequency identification technology is presented. The service is based on hidden Markov models for location within an intelligent building. This problem leads to a multiobjective optimization problem, in which, the best configuration of antennas that minimizes the set of antennas but maximizes the precision of the prediction should be found. Specifically, this study presents a memetic approach for multiobjective improvement of LBS in AmI environments. The memetic algorithm provides, in this problem, the exploitation of domain knowledge and the combination of metaheuristics. Experimental results show that the approach obtains a configuration of antennas which optimally configures the number and position of the antennas while keeping a high quality of the precision of the location prediction. View full abstract»

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  • A Comparative Study of 3-D Face Recognition Under Expression Variations

    Publication Year: 2012 , Page(s): 710 - 727
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1185 KB) |  | HTML iconHTML  

    Research in face recognition has continuously been challenged by extrinsic (head pose, lighting conditions) and intrinsic (facial expression, aging) sources of variability. While many survey papers on face recognition exist, in this paper, we focus on a comparative study of 3-D face recognition under expression variations. As a first contribution, 3-D face databases with expressions are listed, and the most important ones are briefly presented and their complexity is quantified using the iterative closest point (ICP) baseline recognition algorithm. This allows to rank the databases according to their inherent difficulty for face-recognition tasks. This analysis reveals that the FRGC v2 database can be considered as the most challenging because of its size, the presence of expressions and outliers, and the time lapse between the recordings. Therefore, we recommend to use this database as a reference database to evaluate (expression-invariant) 3-D face-recognition algorithms. We also determine and quantify the most important factors that influence the performance. It appears that performance decreases 1) with the degree of nonfrontal pose, 2) for certain expression types, 3) with the magnitude of the expressions, 4) with an increasing number of expressions, and 5) for a higher number of gallery subjects. Future 3-D face-recognition algorithms should be evaluated on the basis of all these factors. As the second contribution, a survey of published 3-D face-recognition methods that deal with expression variations is given. These methods are subdivided into three classes depending on the way the expressions are handled. Region-based methods use expression-stable regions only, while other methods model the expressions either using an isometric or a statistical model. Isometric models assume the deformation because of expression variation to be (locally) isometric, meaning that the deformation preserves lengths along the surface. Statistical models learn how the facial sof- tissue deforms during expressions based on a training database with expression labels. Algorithmic performances are evaluated by the comparison of recognition rates for identification and verification. No statistical significant differences in class performance are found between any pair of classes. View full abstract»

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  • Machine Learning Algorithms in Bipedal Robot Control

    Publication Year: 2012 , Page(s): 728 - 743
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (765 KB) |  | HTML iconHTML  

    Over the past decades, machine learning techniques, such as supervised learning, reinforcement learning, and unsupervised learning, have been increasingly used in the control engineering community. Various learning algorithms have been developed to achieve autonomous operation and intelligent decision making for many complex and challenging control problems. One of such problems is bipedal walking robot control. Although still in their early stages, learning techniques have demonstrated promising potential to build adaptive control systems for bipedal robots. This paper gives a review of recent advances on the state-of-the-art learning algorithms and their applications to bipedal robot control. The effects and limitations of different learning techniques are discussed through a representative selection of examples from the literature. Guidelines for future research on learning control of bipedal robots are provided in the end. View full abstract»

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  • Hybridizing Differential Evolution and Particle Swarm Optimization to Design Powerful Optimizers: A Review and Taxonomy

    Publication Year: 2012 , Page(s): 744 - 767
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1172 KB) |  | HTML iconHTML  

    Differential evolution (DE) and particle swarm optimization (PSO) are two formidable population-based optimizers (POs) that follow different philosophies and paradigms, which are successfully and widely applied in scientific and engineering research. The hybridization between DE and PSO represents a promising way to create more powerful optimizers, especially for specific problem solving. In the past decade, numerous hybrids of DE and PSO have emerged with diverse design ideas from many researchers. This paper attempts to comprehensively review the existing hybrids based on DE and PSO with the goal of collection of different ideas to build a systematic taxonomy of hybridization strategies. Taking into account five hybridization factors, i.e., the relationship between parent optimizers, hybridization level, operating order (OO), type of information transfer (TIT), and type of transferred information (TTI), we propose several classification mechanisms and a versatile taxonomy to differentiate and analyze various hybridization strategies. A large number of hybrids, which include the hybrids of DE and PSO and several other representative hybrids, are categorized according to the taxonomy. The taxonomy can be utilized not only as a tool to identify different hybridization strategies, but also as a reference to design hybrid optimizers. The tradeoff between exploration and exploitation regarding hybridization design is discussed and highlighted. Based on the taxonomy proposed, this paper also indicates several promising lines of research that are worthy of devotion in future. View full abstract»

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  • Autonomous Adaptive and Active Tuning Up of the Dissolved Oxygen Setpoint in a Wastewater Treatment Plant Using Reinforcement Learning

    Publication Year: 2012 , Page(s): 768 - 774
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (975 KB) |  | HTML iconHTML  

    The aim of this paper is to face one of the main problems in the control of wastewater treatment plants (WWTPs). It appears that the control system does not respond as it should because of changes on influent load or flow. In that case, it is required that a plant operator tunes up the parameters of the plant. The dissolved oxygen setpoint is one of those parameters. In this paper, we present a model-free reinforcement learning agent that autonomously learns to actively tune up the oxygen setpoint by itself. By active, we mean continuous, minute after minute, tuning up. By autonomous and adaptive, we mean that the agent learns just by itself from its direct interaction with the WWTP. This agent has been tested with data from the well-known public benchamark simulation model no. 1, and the results that are obtained allow us to conclude that it is possible to build agents that actively and autonomously adapt to each new scenario to control a WWTP. View full abstract»

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  • Developing an Intelligent e-Restaurant With a Menu Recommender for Customer-Centric Service

    Publication Year: 2012 , Page(s): 775 - 787
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (877 KB) |  | HTML iconHTML  

    Traditional restaurant service is typically passive: Waiters must interact with customers directly before processing their orders. However, a high-quality service system should be customer centered; it should immediately recognize customer identities, favorite menus, and expenditure records to provide customer-centric services. To achieve this goal, this study integrates radio frequency identification (RFID), wireless local area network, database technologies, and a menu recommender to develop an intelligent e-restaurant for customer-centric service. This system enables waiters to immediately identify customers via RFID-based membership cards and then actively recommend the most appropriate menus through menu recommender for customers. Experimental results that are obtained from a case study conducted in two Taipei restaurants indicate that the proposed system has practical potential in providing customer-centric service. View full abstract»

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  • Special Issue on Biometric Technology, Systems and Applications

    Publication Year: 2012 , Page(s): 788
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  • IEEE Systems, Man, and Cybernetics Society Information

    Publication Year: 2012 , Page(s): C3
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  • IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews information for authors

    Publication Year: 2012 , Page(s): C4
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Aims & Scope

Overview, tutorial and application papers concerning all areas of interest to the SMC Society: systems engineering, human factors and human machine systems, and cybernetics and computational intelligence. 

Authors should submit human-machine systems papers to the IEEE Transactions on Human-Machine Systems.

Authors should submit systems engineering papers to the IEEE Transactions on Systems, Man and Cybernetics: Systems.

Authors should submit cybernetics papers to the IEEE Transactions on Cybernetics.

Authors should submit social system papers to the IEEE Transactions on Computational Social Systems.

 

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Editor-in-Chief
Dr. Vladimir Marik
(until 31 December 2012)