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Evolutionary Computation, IEEE Transactions on

Issue 4 • Date Aug. 2010

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  • Table of contents

    Publication Year: 2010 , Page(s): C1
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  • IEEE Transactions on Evolutionary Computation publication information

    Publication Year: 2010 , Page(s): C2
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  • Particle Swarm Optimization Aided Orthogonal Forward Regression for Unified Data Modeling

    Publication Year: 2010 , Page(s): 477 - 499
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1561 KB) |  | HTML iconHTML  

    We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leave-one-out (LOO) criteria is developed to construct parsimonious radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines the center vector and diagonal covariance matrix of one RBF node by minimizing the LOO statistics. For regression applications, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification applications. By adopting the Parzen window estimate as the desired response, the unsupervised density estimation problem is transformed into a constrained regression problem. This PSO aided OFR algorithm for tunable-node RBF networks is capable of constructing very parsimonious RBF models that generalize well, and our analysis and experimental results demonstrate that the algorithm is computationally even simpler than the efficient regularization assisted orthogonal least square algorithm based on LOO criteria for selecting fixed-node RBF models. Another significant advantage of the proposed learning procedure is that it does not have learning hyperparameters that have to be tuned using costly cross validation. The effectiveness of the proposed PSO aided OFR construction procedure is illustrated using several examples taken from regression and classification, as well as density estimation applications. View full abstract»

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  • Benefits of a Population: Five Mechanisms That Advantage Population-Based Algorithms

    Publication Year: 2010 , Page(s): 500 - 517
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1318 KB) |  | HTML iconHTML  

    This paper identifies five distinct mechanisms by which a population-based algorithm might have an advantage over a solo-search algorithm in classical optimization. These mechanisms are illustrated through a number of toy problems. Simulations are presented comparing different search algorithms on these problems. The plausibility of these mechanisms occurring in classical optimization problems is discussed. The first mechanism we consider relies on putting together building blocks from different solutions. This is extended to include problems containing critical variables. The second mechanism is the result of focusing of the search caused by crossover. Also discussed in this context is strong focusing produced by averaging many solutions. The next mechanism to be examined is the ability of a population to act as a low-pass filter of the landscape, ignoring local distractions. The fourth mechanism is a population's ability to search different parts of the fitness landscape, thus hedging against bad luck in the initial position or the decisions it makes. The final mechanism is the opportunity of learning useful parameter values to balance exploration against exploitation. View full abstract»

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  • Learning the Large-Scale Structure of the MAX-SAT Landscape Using Populations

    Publication Year: 2010 , Page(s): 518 - 529
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (779 KB) |  | HTML iconHTML  

    A new algorithm for solving maximum satisfiability (MAX-SAT) problems is introduced which clusters good solutions, and restarts the search from the closest feasible solution to the centroid of each cluster. This is shown to be highly efficient for finding good solutions of large MAX-SAT problems. We argue that this success is due to the population learning the large-scale structure of the fitness landscape. Systematic studies of the landscape are presented to support this hypothesis. In addition, a number of other strategies are tested to rule out other possible explanations of the success. Preliminary results are shown, indicating that extensions of the proposed algorithm can give similar improvements on other hard optimization problems. View full abstract»

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  • Incorporating the Notion of Relative Importance of Objectives in Evolutionary Multiobjective Optimization

    Publication Year: 2010 , Page(s): 530 - 546
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (660 KB) |  | HTML iconHTML  

    This paper describes the use of decision maker preferences in terms of the relative importance of objectives in evolutionary multiobjective optimization. A mathematical model of the relative importance of objectives and an elicitation algorithm are proposed, and three methods of incorporating explicated preference information are described and applied to standard test problems in an empirical study. The axiomatic model proposed here formalizes the notion of relative importance of objectives as a partial order that supports strict preference, equality of importance, and incomparability between objective pairs. Unlike most approaches, the proposed model does not encode relative importance as a set of real-valued parameters. Instead, the approach provides a functional correspondence between a coherent overall preference with a subset of the Pareto-optimal front. An elicitation algorithm is also provided to assist a human decision maker in constructing a coherent overall preference. Besides elicitation of a priori preference, an interactive facility is also furnished to enable modification of overall preference while the search progresses. Three techniques of integrating explicated preference information into the well-known Non-dominated Sorting Genetic Algorithm (NSGA)-II are also described and validated in a set of empirical investigation. The approach allows a focus on a subset of the Pareto-front. Validations on test problems demonstrate that the preference-based algorithm gained better convergence as the dimensionality of the problems increased. View full abstract»

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  • XCS for Personalizing Desktop Interfaces

    Publication Year: 2010 , Page(s): 547 - 560
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (634 KB) |  | HTML iconHTML  

    We investigate whether XCS, a genetic algorithm based learning classifier system, can harness information from a user's environment to help desktop applications better personalize themselves to individual users. Specifically, we evaluate XCSs ability to predict user-preferred actions for a calendar and a media player. Results from three real-world user studies indicate that XCS significantly outperforms a decision-tree learner to successfully predict user preferences for these two desktop interfaces. Our results also show that removing external user-related contextual information degrades XCSs performance. This performance degradation emphasizes the need for desktop applications to access external contextual information to better learn user preferences. Our results highlight the potential for a learning classifier systems based approach for personalizing desktop applications to improve the quality of human-computer interaction. View full abstract»

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  • Ensemble of Constraint Handling Techniques

    Publication Year: 2010 , Page(s): 561 - 579
    Cited by:  Papers (47)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (672 KB) |  | HTML iconHTML  

    During the last three decades, several constraint handling techniques have been developed to be used with evolutionary algorithms (EAs). According to the no free lunch theorem, it is impossible for a single constraint handling technique to outperform all other techniques on every problem. In other words, depending on several factors such as the ratio between feasible search space and the whole search space, multimodality of the problem, the chosen EA, and global exploration/local exploitation stages of the search process, different constraint handling methods can be effective during different stages of the search process. Motivated by these observations, we propose an ensemble of constraint handling techniques (ECHT) to solve constrained real-parameter optimization problems, where each constraint handling method has its own population. A distinguishing feature of the ECHT is the usage of every function call by each population associated with each constraint handling technique. Being a general concept, the ECHT can be realized with any existing EA. In this paper, we present two instantiations of the ECHT using four constraint handling methods with the evolutionary programming and differential evolution as the EAs. Experimental results show that the performance of ECHT is better than each single constraint handling method used to form the ensemble with the respective EA, and competitive to the state-of-the-art algorithms. View full abstract»

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  • A Hybrid Evolutionary Approach to the Nurse Rostering Problem

    Publication Year: 2010 , Page(s): 580 - 590
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (634 KB) |  | HTML iconHTML  

    Nurse rostering is an important search problem with many constraints. In the literature, a number of approaches have been investigated including penalty function methods to tackle these constraints within genetic algorithm frameworks. In this paper, we investigate an extension of a previously proposed stochastic ranking method, which has demonstrated superior performance to other constraint handling techniques when tested against a set of constrained optimization benchmark problems. An initial experiment on nurse rostering problems demonstrates that the stochastic ranking method is better at finding feasible solutions, but fails to obtain good results with regard to the objective function. To improve the performance of the algorithm, we hybridize it with a recently proposed simulated annealing hyper-heuristic (SAHH) within a local search and genetic algorithm framework. Computational results show that the hybrid algorithm performs better than both the genetic algorithm with stochastic ranking and the SAHH alone. The hybrid algorithm also outperforms the methods in the literature which have the previously best known results. View full abstract»

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  • From Local Search to Global Conclusions: Migrating Spin Glass-Based Distributed Portfolio Selection

    Publication Year: 2010 , Page(s): 591 - 601
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (822 KB) |  | HTML iconHTML  

    Spin glass optimization is a distributed technique inspired by the interactions in spin glasses in nature. Spin glasses are the lattices of spins where each spin is only a part of the entire solution, in contrast to genetic algorithms (GAs), where each chromosome represents a complete solution. The interaction between spins creates special optimal patterns given appropriate temperature. This optimization paradigm is promising in complex multiobjective optimization tasks because it allows high-computational parallelism among its member spins. Furthermore, since the overall network of spins represents only one solution, there is a great promise in computational efficiency when compared with other population-based/stochastic approaches such as GAs and simulated annealing. The nature of this method is also entirely different from other distributed frameworks such as Hopfield neural network since spins' paradigm of interaction does not have to be fully connected; i.e., the neighborhoods of interactions can expand or collapse, hence less computation and better convergence speed. In this paper, we apply a heuristic method based on the spin glass model that uses migration and elitism operators in addition to temperature control in order to trace out an efficient frontier in the optimization landscape. The proposed methodology is then applied to the problem of portfolio selection. Portfolio selection is one of the nondeterministic polynomial complete problems where each asset's behavior is similar to spin's behavior and it is therefore suitable as a case study. We show that, in proper circumstances, decrementing local energy of each spin can decrement global energy of the glass, and correspondingly, if the optimization problem can be suitably mapped to the glass, the expected cost function decreases. View full abstract»

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  • Evolutionary Optimization of Service Times in Interactive Voice Response Systems

    Publication Year: 2010 , Page(s): 602 - 617
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (808 KB) |  | HTML iconHTML  

    A call center is a system used by companies to provide a number of services to customers, which may vary from providing simple information to gathering and dealing with complaints or more complex transactions. The design of this kind of system is an important task, since the trend is that companies and institutions choose call centers as the primary option for customer relationship management. This paper presents an evolutionary algorithm based on Dandelion encoding to obtain near-optimal service trees which represent the structure of the desired call center. We introduce several modifications to the original Dandelion encoding in order to adapt it to the specific problem of service tree design. Two search space size reduction procedures improve the performance of the algorithm. Systematic experiments have been tackled in order to show the performance of our approach: first, we tackle different synthetic instances, where we discuss and analyze several aspects of the proposed evolutionary algorithm, and second, we tackle a real application, the design of the call center of an Italian telecommunications company. In all the experiments carried out we compare our approach with a lower bound for the problem based on information theory, and also with the results of a Huffman algorithm we have used for reference. View full abstract»

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  • A Study of Multiobjective Metaheuristics When Solving Parameter Scalable Problems

    Publication Year: 2010 , Page(s): 618 - 635
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (881 KB) |  | HTML iconHTML  

    To evaluate the search capabilities of a multiobjective algorithm, the usual approach is to choose a benchmark of known problems, to perform a fixed number of function evaluations, and to apply a set of quality indicators. However, while real problems could have hundreds or even thousands of decision variables, current benchmarks are normally adopted with relatively few decision variables (normally from 10 to 30). Furthermore, performing a constant number of evaluations does not provide information about the effort required by an algorithm to get a satisfactory set of solutions; this information would also be of interest in real scenarios, where evaluating the functions defining the problem can be computationally expensive. In this paper, we study the effect of parameter scalability in a number of state-of-the-art multiobjective metaheuristics. We adopt a benchmark of parameter-wise scalable problems (the Zitzler-Deb-Thiele test suite) and analyze the behavior of eight multiobjective metaheuristics on these test problems when using a number of decision variables that range from 8 up to 2048. By using the hypervolume indicator as a stopping condition, we also analyze the computational effort required by each algorithm in order to reach the Pareto front. We conclude that the two analyzed algorithms based on particle swarm optimization and differential evolution yield the best overall results. View full abstract»

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  • A Territory Defining Multiobjective Evolutionary Algorithms and Preference Incorporation

    Publication Year: 2010 , Page(s): 636 - 664
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2116 KB) |  | HTML iconHTML  

    We have developed a steady-state elitist evolutionary algorithm to approximate the Pareto-optimal frontiers of multiobjective decision making problems. The algorithms define a territory around each individual to prevent crowding in any region. This maintains diversity while facilitating the fast execution of the algorithm. We conducted extensive experiments on a variety of test problems and demonstrated that our algorithm performs well against the leading multiobjective evolutionary algorithms. We also developed a mechanism to incorporate preference information in order to focus on the regions that are appealing to the decision maker. Our experiments show that the algorithm approximates the Pareto-optimal solutions in the desired region very well when we incorporate the preference information. View full abstract»

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  • Erratum to “Niching Without Niching Parameters: Particle Swarm Optimization Using a Ring Topology” [Feb 10 150-169]

    Publication Year: 2010 , Page(s): 665
    Cited by:  Papers (38)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (37 KB)  

    In the above titled paper (ibid., vol. 14, no. 1, pp. 150-169, Feb. 10), some of the results in Tables IV and VI were incorrect. An explanation is presented here. View full abstract»

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    Publication Year: 2010 , Page(s): 666
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  • IEEE 2009 Membership Application

    Publication Year: 2010 , Page(s): 667 - 668
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2010 , Page(s): C3
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  • IEEE Transactions on Evolutionary Computation Information for authors

    Publication Year: 2010 , 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

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