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Evolutionary Computation, IEEE Transactions on

Issue 3 • Date June 2013

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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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  • Stochastic Diversity Loss and Scalability in Estimation of Distribution Genetic Programming

    Page(s): 301 - 320
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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (689 KB) |  | HTML iconHTML  

    In estimation of distribution algorithms (EDAs), probability models hold accumulating evidence on the location of an optimum. Stochastic sampling drift has been heavily researched in EDA optimization but not in EDAs applied to genetic programming (EDA-GP). We show that, for EDA-GPs using probabilistic prototype tree models, stochastic drift in sampling and selection is a serious problem, inhibiting scaling to complex problems. Problems requiring deep dependence in their probability structure see such rapid stochastic drift that the usual methods for controlling drift are unable to compensate. We propose a new alternative, analogous to likelihood weighting of evidence. We demonstrate in a small-scale experiment that it does counteract the drift, sufficiently to leave EDA-GP systems subject to similar levels of stochastic drift to other EDAs. View full abstract»

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  • A Survey on Multiobjective Evolutionary Algorithms for the Solution of the Portfolio Optimization Problem and Other Finance and Economics Applications

    Page(s): 321 - 344
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (681 KB) |  | HTML iconHTML  

    The coinciding development of multiobjective evolutionary algorithms (MOEAs) and the emergence of complex problem formulation in the finance and economics areas has led to a mutual interest from both research communities. Since the 1990s, an increasing number of works have thus proposed the application of MOEAs to solve complex financial and economic problems, involving multiple objectives. This paper provides a survey on the state-of-the-art of research, reported in the specialized literature to date, related to this framework. The taxonomy chosen here makes a distinction between the (widely covered) portfolio optimization problem and the other applications in the field. In addition, potential paths for future research within this area are identified. View full abstract»

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  • A Note on Generalization Loss When Evolving Adaptive Pattern Recognition Systems

    Page(s): 345 - 352
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1276 KB) |  | HTML iconHTML  

    Evolutionary computing provides powerful methods for designing pattern recognition systems. This design process is typically based on finite sample data and therefore bears the risk of overfitting. This paper aims at raising the awareness of various types of overfitting and at providing guidelines for how to deal with them. We restrict our considerations to the predominant scenario in which fitness computations are based on point estimates. Three different sources of losing generalization performance when evolving learning machines, namely overfitting to training, test, and final selection data, are identified, discussed, and experimentally demonstrated. The importance of a pristine hold-out data set for the selection of the final result from the evolved candidates is highlighted. It is shown that it may be beneficial to restrict this last selection process to a subset of the evolved candidates. View full abstract»

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  • A Novel Genetic Programming Approach for Frequency-Dependent Modeling

    Page(s): 353 - 367
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (12541 KB) |  | HTML iconHTML  

    Frequency-dependent modeling of devices and systems is a common practice in several fields, such as power systems, microwave systems, and electronics systems. The modeling process usually involves converting the tabulated frequency-response data into a compact equivalent circuit model. The main drawback of the currently existing methods such as vector fitting is that the obtained model is often nonpassive, leading to unstable simulations. In order to overcome this problem, this paper proposes a genetic programming (GP) approach to generate equivalent circuits with guaranteed passivity. The proposed method starts with a nonoptimal initial equivalent circuit. Both the elements and the topology of this circuit are then evolved by the proposed GP-based method, and an accurate equivalent circuit is obtained. Key ideas and detailed algorithms are presented in this paper. Finally, the performance of the proposed method is verified by using different case studies. View full abstract»

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  • Evolving Diverse Ensembles Using Genetic Programming for Classification With Unbalanced Data

    Page(s): 368 - 386
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4136 KB) |  | HTML iconHTML  

    In classification, machine learning algorithms can suffer a performance bias when data sets are unbalanced. Data sets are unbalanced when at least one class is represented by only a small number of training examples (called the minority class), while the other class(es) make up the majority. In this scenario, classifiers can have good accuracy on the majority class, but very poor accuracy on the minority class(es). This paper proposes a multiobjective genetic programming (MOGP) approach to evolving accurate and diverse ensembles of genetic program classifiers with good performance on both the minority and majority of classes. The evolved ensembles comprise of nondominated solutions in the population where individual members vote on class membership. This paper evaluates the effectiveness of two popular Pareto-based fitness strategies in the MOGP algorithm (SPEA2 and NSGAII), and investigates techniques to encourage diversity between solutions in the evolved ensembles. Experimental results on six (binary) class imbalance problems show that the evolved ensembles outperform their individual members, as well as single-predictor methods such as canonical GP, naive Bayes, and support vector machines, on highly unbalanced tasks. This highlights the importance of developing an effective fitness evaluation strategy in the underlying MOGP algorithm to evolve good ensemble members. View full abstract»

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  • A Distance-Based Locally Informed Particle Swarm Model for Multimodal Optimization

    Page(s): 387 - 402
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6363 KB) |  | HTML iconHTML  

    Multimodal optimization amounts to finding multiple global and local optima (as opposed to a single solution) of a function, so that the user can have a better knowledge about different optimal solutions in the search space and when needed, the current solution may be switched to a more suitable one while still maintaining the optimal system performance. Niching particle swarm optimizers (PSOs) have been widely used by the evolutionary computation community for solving real-parameter multimodal optimization problems. However, most of the existing PSO-based niching algorithms are difficult to use in practice because of their poor local search ability and requirement of prior knowledge to specify certain niching parameters. This paper has addressed these issues by proposing a distance-based locally informed particle swarm (LIPS) optimizer, which eliminates the need to specify any niching parameter and enhance the fine search ability of PSO. Instead of using the global best particle, LIPS uses several local bests to guide the search of each particle. LIPS can operate as a stable niching algorithm by using the information provided by its neighborhoods. The neighborhoods are estimated in terms of Euclidean distance. The algorithm is compared with a number of state-of-the-art evolutionary multimodal optimizers on 30 commonly used multimodal benchmark functions. The experimental results suggest that the proposed technique is able to provide statistically superior and more consistent performance over the existing niching algorithms on the test functions, without incurring any severe computational burdens. View full abstract»

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  • Genetic Variation and the Evolution of Consensus in Digital Organisms

    Page(s): 403 - 417
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6301 KB) |  | HTML iconHTML  

    In this paper, we describe a study of the evolution of consensus, a cooperative behavior in which members in both homogeneous and heterogeneous groups, must agree on information sensed in their environment. We conducted the study using digital evolution, a form of evolutionary computation where a population of computer programs (digital organisms) exists in a user-defined computational environment and is subject to instruction-level mutations and natural selection. We placed these digital organisms into groups whose fitness relied upon their ability to perform consensus. We then tested different degrees and types of genetic variation present in the population, based on biologically inspired models of gene flow, including mutation, sexual recombination, migration, and horizontal gene transfer. Our experimental treatments examined the effect of these processes on genetic variation and groups' ability to reach consensus. The results of these experiments demonstrate that while genetic heterogeneity within groups increases the difficulty of the consensus task, a surprising number of groups were able to overcome these obstacles and evolve this cooperative behavior. View full abstract»

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  • A New Method for Lower Bounds on the Running Time of Evolutionary Algorithms

    Page(s): 418 - 435
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (967 KB) |  | HTML iconHTML  

    In this paper a new method for proving lower bounds on the expected running time of evolutionary algorithms (EAs) is presented. It is based on fitness-level partitions and an additional condition on transition probabilities between fitness levels. The method is versatile, intuitive, elegant, and very powerful. It yields exact or near-exact lower bounds for LO, OneMax, long $k$-paths, and all functions with a unique optimum. Most lower bounds are very general; they hold for all EAs that only use bit-flip mutation as variation operator, i.e., for all selection operators and population models. The lower bounds are stated with their dependence on the mutation rate. These results have very strong implications. They allow us to determine the optimal mutation-based algorithm for LO and OneMax, i.e., the algorithm that minimizes the expected number of fitness evaluations. This includes the choice of the optimal mutation rate. View full abstract»

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  • A New Particle Swarm Optimization Method Enhanced With a Periodic Mutation Strategy and Neural Networks

    Page(s): 436 - 452
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (9536 KB) |  | HTML iconHTML  

    Particle swarm optimization (PSO), a relatively new population-based intelligence algorithm, exhibits good performance on optimization problems. However, during the optimization process, the particles become more and more similar, and gather into the neighborhood of the best particle in the swarm, which makes the swarm prematurely converged most likely around the local solution. A new optimization algorithm called multifrequency vibrational PSO is significantly improved and tested for two different test cases: optimization of six different benchmark test functions and direct shape optimization of an airfoil in transonic flow. The algorithm emphasizes a new mutation application strategy and diversity variety, such as global random diversity and local controlled diversity. The results offer insight into how the mutation operator affects the nature of the diversity and objective function value. The local controlled diversity is based on an artificial neural network. As far as both the demonstration cases' problems are considered, remarkable reductions in the computational times have been accomplished. View full abstract»

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  • Special Issue on Theoretical Foundations of Evolutionary Computation

    Page(s): 453
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  • Together, we are advancing technology

    Page(s): 454
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  • IEEE Xplore Digital Library

    Page(s): 455
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  • Quality without compromise

    Page(s): 456
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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