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Evolutionary Computation, IEEE Transactions on

Issue 4 • Date Aug. 2012

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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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  • Solving Multicommodity Capacitated Network Design Problems Using Multiobjective Evolutionary Algorithms

    Page(s): 449 - 471
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4817 KB) |  | HTML iconHTML  

    Evolutionary algorithms can be applied to a variety of constrained network communication problems with centric type models. This paper shows that with real-world complex network communication problems of this type, sophisticated statistical search is required. This situation occurs due to the fact that these optimization problems are at least NP-complete. In order to appreciate the formal modeling of realistic communication networks, historical network design problems are presented and evolved into more complex real-world models with associated deterministic and stochastic solution approaches discussed. This discussion leads into the design of an innovative multiobjective evolutionary algorithm (MOEA) to solve a very complex network design problem variation called the multicommodity capacitated network design problem (MCNDP). This variation represents a hybrid real-world communication architecture as reflected in real-world network centric models with directional communications, multiple objectives including costs, delays, robustness, vulnerability, and operating reliability within network constraints. Nodes in such centric systems can have multiple and varying link capacities as well as rates and information (commodity) quantities to be sent and received. Each commodity can also have independent prioritized bandwidth and spectrum requirements. The nondominated sorting genetic algorithm (NSGA-II) is selected as the MOEA framework which is modified and parallelized to solve the generic MCNDP. Since the MCNDP is highly constrained but with an enormous number of possible network communication topologies, a novel initialization procedure and mutation method are integrated resulting in reduced search space. Empirical results and analysis indicate that effective topological Pareto solutions are generated for use in highly constrained, communication-based network design. View full abstract»

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  • An Integrated Neuroevolutionary Approach to Reactive Control and High-Level Strategy

    Page(s): 472 - 488
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (8157 KB) |  | HTML iconHTML  

    One promising approach to general-purpose artificial intelligence is neuroevolution, which has worked well on a number of problems from resource optimization to robot control. However, state-of-the-art neuroevolution algorithms like neuroevolution of augmenting topologies (NEAT) have surprising difficulty on problems that are fractured, i.e., where the desired actions change abruptly and frequently. Previous work demonstrated that bias and constraint (e.g., RBF-NEAT and Cascade-NEAT algorithms) can improve learning significantly on such problems. However, experiments in this paper show that relatively unrestricted algorithms (e.g., NEAT) still yield the best performance on problems requiring reactive control. Ideally, a single algorithm would be able to perform well on both fractured and unfractured problems. This paper introduces such an algorithm called SNAP-NEAT that uses adaptive operator selection to integrate strengths of NEAT, RBF-NEAT, and Cascade-NEAT. SNAP-NEAT is evaluated empirically on a set of problems ranging from reactive control to high-level strategy. The results show that SNAP-NEAT can adapt intelligently to the type of problem that it faces, thus laying the groundwork for learning algorithms that can be applied to a wide variety of problems. View full abstract»

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  • A Process Algebra Genetic Algorithm

    Page(s): 489 - 503
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3416 KB) |  | HTML iconHTML  

    A genetic algorithm that utilizes process algebra for coding of solution chromosomes and for defining evolutionary based operators is presented. The algorithm is applicable to mission planning and optimization problems. As an example the high level mission planning for a cooperative group of uninhabited aerial vehicles is investigated. The mission planning problem is cast as an assignment problem, and solutions to the assignment problem are given in the form of chromosomes that are manipulated by evolutionary operators. The evolutionary operators of crossover and mutation are formally defined using the process algebra methodology, along with specific algorithms needed for their execution. The viability of the approach is investigated using simulations and the effectiveness of the algorithm is shown in small, medium, and large scale problems. View full abstract»

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  • Using the Averaged Hausdorff Distance as a Performance Measure in Evolutionary Multiobjective Optimization

    Page(s): 504 - 522
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5655 KB) |  | HTML iconHTML  

    The Hausdorff distance dH is a widely used tool to measure the distance between different objects in several research fields. Possible reasons for this might be that it is a natural extension of the well-known and intuitive distance between points and/or the fact that dH defines in certain cases a metric in the mathematical sense. In evolutionary multiobjective optimization (EMO) the task is typically to compute the entire solution set-the so-called Pareto set-respectively its image, the Pareto front. Hence, dH should, at least at first sight, be a natural choice to measure the performance of the outcome set in particular since it is related to the terms spread and convergence as used in EMO literature. However, so far, dH does not find the general approval in the EMO community. The main reason for this is that dH penalizes single outliers of the candidate set which does not comply with the use of stochastic search algorithms such as evolutionary strategies. In this paper, we define a new performance indicator, Δp, which can be viewed as an “averaged Hausdorff distance” between the outcome set and the Pareto front and which is composed of (slight modifications of) the well-known indicators generational distance (GD) and inverted generational distance (IGD). We will discuss theoretical properties of Δp (as well as for GD and IGD) such as the metric properties and the compliance with state-of-theart multiobjective evolutionary algorithms (MOEAs), and will further on demonstrate by empirical results the potential of Δp as a new performance indicator for the evaluation of MOEAs. View full abstract»

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  • Promoting Creative Design in Interactive Evolutionary Computation

    Page(s): 523 - 536
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (34398 KB) |  | HTML iconHTML  

    We use a new measure of creativity as a guide in an interactive evolutionary art task and tie the results to natural language usage of the term “creative.” Following previous work, we explore a tractable definition of creativity, one emphasizing the novelty of systems, and its addition to an interactive application. We next introduce a generative ecosystemic art system, EvoEco, an agent-based pixel-level means of generating images. EvoEco is used as a component of an online survey which asks users to evolve a pleasing image and then rank the success of the process and its output. Evolutionary search is augmented with the creativity measure, and compared with control groups augmented with either random search or a measure of phenotypic distance. We show that users consistently rate the creativity measure-enhanced version as more “creative” and “novel” than other search techniques. We further derive additional insights into appropriate forms of genetic representation and pattern space-traversal in an interactive evolutionary algorithm. View full abstract»

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  • Effects of Iterated Interactions in Multiplayer Spatial Evolutionary Games

    Page(s): 537 - 555
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (16346 KB) |  | HTML iconHTML  

    Mechanisms promoting the evolution of cooperation in two players and two strategies (22) evolutionary games have been investigated in great detail over the past decades. Understanding the effects of repeated interactions in multiplayer spatial games, however, is a formidable challenge. In this paper, we present a multiplayer evolutionary game model in which agents play iterative games in spatial populations. -player versions of the well-known Prisoner's Dilemma and the Snowdrift games are used as the basis of the investigation. These games were chosen as they have emerged as the most promising mathematical metaphors for studying cooperative phenomena. Here, we have adopted an experimental approach to study the emergent behavior, exploring different parameter configurations via numerical simulations. Key model parameters include the cost-to-benefit ratio, the size of groups, the number of repeated encounters, and the interaction topology. Our simulation results reveal that, while the introduction of iterated interactions does promote higher levels of cooperative behavior across a wide range of parameter settings, the cost-to-benefit ratio and group size are important factors in determining the appropriate length of beneficial repeated interactions. In particular, increasing the number of iterated interactions may have a detrimental effect when the cost-to-benefit ratio and group size are small. View full abstract»

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  • A General Framework of Multipopulation Methods With Clustering in Undetectable Dynamic Environments

    Page(s): 556 - 577
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3517 KB) |  | HTML iconHTML  

    To solve dynamic optimization problems, multiple population methods are used to enhance the population diversity for an algorithm with the aim of maintaining multiple populations in different subareas in the fitness landscape. Many experimental studies have shown that locating and tracking multiple relatively good optima rather than a single global optimum is an effective idea in dynamic environments. However, several challenges need to be addressed when multipopulation methods are applied, e.g., how to create multiple populations, how to maintain them in different subareas, and how to deal with the situation where changes cannot be detected or predicted. To address these issues, this paper investigates a hierarchical clustering method to locate and track multiple optima for dynamic optimization problems. To deal with undetectable dynamic environments, this paper applies the random immigrants method without change detection based on a mechanism that can automatically reduce redundant individuals in the search space throughout the run. These methods are implemented into several research areas, including particle swarm optimization, genetic algorithm, and differential evolution. An experimental study is conducted based on the moving peaks benchmark to test the performance with several other algorithms from the literature. The experimental results show the efficiency of the clustering method for locating and tracking multiple optima in comparison with other algorithms based on multipopulation methods on the moving peaks benchmark. View full abstract»

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  • On the Design of Constraint Covariance Matrix Self-Adaptation Evolution Strategies Including a Cardinality Constraint

    Page(s): 578 - 596
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (15787 KB) |  | HTML iconHTML  

    This paper describes the algorithm's engineering of a covariance matrix self-adaptation evolution strategy (CMSA-ES) for solving a mixed linear/nonlinear constrained optimization problem arising in portfolio optimization. While the feasible solution space is defined by the (probabilistic) simplex, the nonlinearity comes in by a cardinality constraint bounding the number of linear inequalities violated. This gives rise to a nonconvex optimization problem. The design is based on the CMSA-ES and relies on three specific techniques to fulfill the different constraints. The resulting algorithm is then thoroughly tested on a data set derived from time series data of the Dow Jones Index. View full abstract»

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  • Special Issue on Advances in Multiobjective Evolutionary Algorithms for Data Minin

    Page(s): 597
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  • Special Issue on Evolutionary Computation in Finance, Economics, and Management Sciences

    Page(s): 598
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  • IEEE copyright form

    Page(s): 599 - 600
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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

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
Garrison W. Greenwood, Ph.D. P.E
Portland State University