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Evolutionary Computation, IEEE Transactions on

Issue 2 • Date April 2011

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  • Table of contents

    Publication Year: 2011 , Page(s): C1
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  • IEEE Transactions on Evolutionary Computation publication information

    Publication Year: 2011 , Page(s): C2
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  • Efficient Hybrid-Game Strategies Coupled to Evolutionary Algorithms for Robust Multidisciplinary Design Optimization in Aerospace Engineering

    Publication Year: 2011 , Page(s): 133 - 150
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3856 KB) |  | HTML iconHTML  

    A number of game strategies have been developed in past decades and used in the fields of economics, engineering, computer science, and biology due to their efficiency in solving design optimization problems. In addition, research in multiobjective and multidisciplinary design optimization has focused on developing a robust and efficient optimization method so it can produce a set of high quality solutions with less computational time. In this paper, two optimization techniques are considered; the first optimization method uses multifidelity hierarchical Pareto-optimality. The second optimization method uses the combination of game strategies Nash-equilibrium and Pareto-optimality. This paper shows how game strategies can be coupled to multiobjective evolutionary algorithms and robust design techniques to produce a set of high quality solutions. Numerical results obtained from both optimization methods are compared in terms of computational expense and model quality. The benefits of using Hybrid and non-Hybrid-Game strategies are demonstrated. View full abstract»

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  • Decomposition-Based Memetic Algorithm for Multiobjective Capacitated Arc Routing Problem

    Publication Year: 2011 , Page(s): 151 - 165
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1018 KB) |  | HTML iconHTML  

    The capacitated arc routing problem (CARP) is a challenging combinatorial optimization problem with many real-world applications, e.g., salting route optimization and fleet management. There have been many attempts at solving CARP using heuristic and meta-heuristic approaches, including evolutionary algorithms. However, almost all such attempts formulate CARP as a single-objective problem although it usually has more than one objective, especially considering its real-world applications. This paper studies multiobjective CARP (MO-CARP). A new memetic algorithm (MA) called decomposition-based MA with extended neighborhood search (D-MAENS) is proposed. The new algorithm combines the advanced features from both the MAENS approach for single-objective CARP and multiobjective evolutionary optimization. Our experimental studies have shown that such combination outperforms significantly an off-the-shelf multiobjective evolutionary algorithm, namely nondominated sorting genetic algorithm II, and the state-of-the-art multiobjective algorithm for MO-CARP (LMOGA). Our work has also shown that a specifically designed multiobjective algorithm by combining its single-objective version and multiobjective features may lead to competitive multiobjective algorithms for multiobjective combinatorial optimization problems. View full abstract»

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  • Flight of the FINCH Through the Java Wilderness

    Publication Year: 2011 , Page(s): 166 - 182
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2290 KB) |  | HTML iconHTML  

    We describe Fertile Darwinian Bytecode Harvester (FINCH), a methodology for evolving Java bytecode, enabling the evolution of extant, unrestricted Java programs, or programs in other languages that compile to Java bytecode. Our approach is based upon the notion of compatible crossover, which produces correct programs by performing operand stack-based, local variables-based, and control flow-based compatibility checks on source and destination bytecode sections. This is in contrast to existing work that uses restricted subsets of the Java bytecode instruction set as a representation language for individuals in genetic programming. We demonstrate FINCH's unqualified success at solving a host of problems, including simple and complex regression, trail navigation, image classification, array sum, and tic-tac-toe. FINCH exploits the richness of the Java virtual machine architecture and type system, ultimately evolving human-readable solutions in the form of Java programs. The ability to evolve Java programs will hopefully lead to a valuable new tool in the software engineer's toolkit. View full abstract»

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  • Diversity Management in Evolutionary Many-Objective Optimization

    Publication Year: 2011 , Page(s): 183 - 195
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (884 KB) |  | HTML iconHTML  

    In evolutionary multiobjective optimization, the task of the optimizer is to obtain an accurate and useful approximation of the true Pareto-optimal front. Proximity to the front and diversity of solutions within the approximation set are important requirements. Most established multiobjective evolutionary algorithms (MOEAs) have mechanisms that address these requirements. However, in many-objective optimization, where the number of objectives is greater than 2 or 3, it has been found that these two requirements can conflict with one another, introducing problems such as dominance resistance and speciation. In this paper, two diversity management mechanisms are introduced to investigate their impact on overall solution convergence. They are introduced separately, and in combination, and tested on a set of test functions with an increasing number of objectives (6-20). It is found that the inclusion of one of the mechanisms improves the performance of a well-established MOEA in many-objective optimization problems, in terms of both convergence and diversity. The relevance of this for many-objective MOEAs is discussed. View full abstract»

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  • Systematic Initialization Techniques for Hybrid Evolutionary Algorithms for Solving Two-Stage Stochastic Mixed-Integer Programs

    Publication Year: 2011 , Page(s): 196 - 214
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1488 KB) |  | HTML iconHTML  

    This paper introduces new initialization approaches for evolutionary algorithms that solve two-stage stochastic mixed-integer problems. The two-stage stochastic mixed-integer programs are handled by a stage decomposition based hybrid algorithm where an evolutionary algorithm handles the first-stage decisions and mathematical programming handles the second-stage decisions. The population of the evolutionary algorithm is initialized by using solutions which are generated in a preprocessing step of the hybrid algorithm. This paper presents three different initialization approaches in which the two-stage stochastic mixed-integer program is exploited in order to obtain potentially good starting solutions for the evolutionary algorithm. In case of infeasible initializations, the population is driven toward feasibility by a penalty function. Comparisons of an evolutionary algorithm with a classical random initialization and the new initialization approaches for two real-world problems show that the new initialization approaches lead to high quality feasible solutions in significantly shorter computing times. View full abstract»

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  • Autonomous Virulence Adaptation Improves Coevolutionary Optimization

    Publication Year: 2011 , Page(s): 215 - 229
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1039 KB) |  | HTML iconHTML  

    A novel approach for the autonomous virulence adaptation (AVA) of competing populations in a coevolutionary optimization framework is presented. Previous work has demonstrated that setting an appropriate virulence, v, of populations accelerates coevolutionary optimization by avoiding detrimental periods of disengagement. However, since the likelihood of disengagement varies both between systems and over time, choosing the ideal value of v is problematic. The AVA technique presented here uses a machine learning approach to continuously tune v as system engagement varies. In a simple, abstract domain, AVA is shown to successfully adapt to the most productive values of v. Further experiments, in more complex domains of sorting networks and maze navigation, demonstrate AVA's efficiency over reduced virulence and the layered Pareto coevolutionary archive. View full abstract»

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  • Toward an Evolutionary Computing Modeling Language

    Publication Year: 2011 , Page(s): 230 - 247
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1808 KB) |  | HTML iconHTML  

    The importance of domain knowledge in the design of effective evolutionary algorithms (EAs) is widely acknowledged in the meta-heuristics community. In the last few decades, a plethora of EAs has been manually designed by domain experts for solving domain-specific problems. Specialization has been achieved mainly by embedding available domain knowledge into the algorithms. Although programming libraries have been made available to construct EAs, a unifying framework for designing specialized EAs across different problem domains and branches of evolutionary computing does not exist yet. In this paper, we address this issue by introducing an evolutionary computing modeling language (ECML) which is based on the unified modeling language (UML). ECML incorporates basic UML elements and introduces new extensions that are specially needed for the evolutionary computation domain. Subsequently, the concept of meta evolutionary algorithms (MEAs) is introduced as a family of EAs that is capable of interpreting ECML. MEAs are solvers that are not restricted to a particular problem domain or branch of evolutionary computing through the use of ECML. By separating problem-specific domain knowledge from the EA implementation, we show that a unified framework for evolutionary computation can be attained. We demonstrate our approach by applying it to a number of examples. View full abstract»

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  • Theoretical Analysis of Phenotypic Diversity in Real-Valued Evolutionary Algorithms With More-Than-One-Element Replacement

    Publication Year: 2011 , Page(s): 248 - 266
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2653 KB) |  | HTML iconHTML  

    This paper investigates the evolutionary dynamics of steady-state real-valued evolutionary algorithms (RVEAs) with more-than-one-element replacement theoretically, whereas most theoretical studies of RVEAs have considered single-or all-element replacement. The subject RVEAs are of interest because they appear in various fashions, such as real-coded genetic algorithms (RCGAs) and island RVEAs. The analysis is conducted to deepen the understanding of how RVEA components and their parameters influence the phenotypic diversity in the parental pool. First, the diversity evolution is modeled mathematically and then a constraint of diversity control is derived from this model. The control method is demonstrated and the accuracy of the theoretical predictions is evaluated through experiments. The shortest convergence time is estimated. The analysis requires few assumptions about either the variation operators or selection schemes, and therefore is applicable to various RVEAs. As such an application in RCGAs, the influence on the diversity evolution of offspring-population size, parental-pool size, crossover-operator parameter, and selection-pressure parameters of two selection mechanisms is quantified. The computational efficiency, search stability, and selection-pressure controllability are then evaluated. The analysis results are discussed from a practical point of view in parameter settings for preventing premature convergence. View full abstract»

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  • An Inflationary Differential Evolution Algorithm for Space Trajectory Optimization

    Publication Year: 2011 , Page(s): 267 - 281
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1467 KB) |  | HTML iconHTML  

    In this paper, we define a discrete dynamical system that governs the evolution of a population of agents. From the dynamical system, a variant of differential evolution (DE) is derived. It is then demonstrated that, under some assumptions on the differential mutation strategy and on the local structure of the objective function, the proposed dynamical system has fixed points toward which it converges with probability one for an infinite number of generations. This property is used to derive an algorithm that performs better than standard DE on some space trajectory optimization problems. The novel algorithm is then extended with a guided restart procedure that further increases the performance, reducing the probability of stagnation in deceptive local minima. View full abstract»

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    Publication Year: 2011 , Page(s): 282
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  • 2011 IEEE membership form

    Publication Year: 2011 , Page(s): 283 - 284
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2011 , Page(s): C3
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  • IEEE Transactions on Evolutionary Computation Information for authors

    Publication Year: 2011 , 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.
 

Full Aims & Scope

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