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Evolutionary Computation, IEEE Transactions on

Issue 3 • Date June 2010

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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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  • Generalizing Surrogate-Assisted Evolutionary Computation

    Page(s): 329 - 355
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2084 KB) |  | HTML iconHTML  

    Using surrogate models in evolutionary search provides an efficient means of handling today's complex applications plagued with increasing high-computational needs. Recent surrogate-assisted evolutionary frameworks have relied on the use of a variety of different modeling approaches to approximate the complex problem landscape. From these recent studies, one main research issue is with the choice of modeling scheme used, which has been found to affect the performance of evolutionary search significantly. Given that theoretical knowledge available for making a decision on an approximation model a priori is very much limited, this paper describes a generalization of surrogate-assisted evolutionary frameworks for optimization of problems with objectives and constraints that are computationally expensive to evaluate. The generalized evolutionary framework unifies diverse surrogate models synergistically in the evolutionary search. In particular, it focuses on attaining reliable search performance in the surrogate-assisted evolutionary framework by working on two major issues: 1) to mitigate the 'curse of uncertainty' robustly, and 2) to benefit from the 'bless of uncertainty.' The backbone of the generalized framework is a surrogate-assisted memetic algorithm that conducts simultaneous local searches using ensemble and smoothing surrogate models, with the aims of generating reliable fitness prediction and search improvements simultaneously. Empirical study on commonly used optimization benchmark problems indicates that the generalized framework is capable of attaining reliable, high quality, and efficient performance under a limited computational budget. View full abstract»

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  • An Evolutionary Approach to the Multidepot Capacitated Arc Routing Problem

    Page(s): 356 - 374
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1308 KB) |  | HTML iconHTML  

    The capacitated arc routing problem (CARP) is a challenging vehicle routing problem with numerous real world applications. In this paper, an extended version of CARP, the multidepot capacitated arc routing problem (MCARP), is presented to tackle practical requirements. Existing CARP heuristics are extended to cope with MCARP and are integrated into a novel evolutionary framework: the initial population is constructed either by random generation, the extended random path-scanning heuristic, or the extended random Ulusoy's heuristic. Subsequently, multiple distinct operators are employed to perform selection, crossover, and mutation. Finally, the partial replacement procedure is implemented to maintain population diversity. The proposed evolutionary approach (EA) is primarily characterized by the exploitation of attributes found in near-optimal MCARP solutions that are obtained throughout the execution of the algorithm. Two techniques are employed toward this end: the performance information of an operator is applied to select from a range of operators for selection, crossover, and mutation. Furthermore, the arc assignment priority information is employed to determine promising positions along the genome for operations of crossover and mutation. The EA is evaluated on 107 instances with up to 140 nodes and 380 arcs. The experimental results suggest that the integrated evolutionary framework significantly outperforms these individual extended heuristics. View full abstract»

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  • Optimizing Capacitance Ratio Assignment for Low-Sensitivity SC Filter Implementation

    Page(s): 375 - 380
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (630 KB) |  | HTML iconHTML  

    Accurate capacitance matching is essential for switched-capacitor filters implementation because the filter coefficients depend upon the capacitance ratios. The use of identical unit capacitors in parallel to form larger capacitances and careful layout design can provide, in many cases, an accuracy of 0.1%. Unfortunately, this technique can be directly applied only if the filter coefficients are rational numbers. In general, coefficient approximations are required, leading to frequency response errors. In this paper, a new design method, using a genetic algorithm, is proposed to find the optimum capacitance ratio approximations by rational numbers which minimize the total number of unit capacitors for a given acceptable frequency response error, in order to save die area. View full abstract»

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  • Chemical-Reaction-Inspired Metaheuristic for Optimization

    Page(s): 381 - 399
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (758 KB) |  | HTML iconHTML  

    We encounter optimization problems in our daily lives and in various research domains. Some of them are so hard that we can, at best, approximate the best solutions with (meta-) heuristic methods. However, the huge number of optimization problems and the small number of generally acknowledged methods mean that more metaheuristics are needed to fill the gap. We propose a new metaheuristic, called chemical reaction optimization (CRO), to solve optimization problems. It mimics the interactions of molecules in a chemical reaction to reach a low energy stable state. We tested the performance of CRO with three nondeterministic polynomial-time hard combinatorial optimization problems. Two of them were traditional benchmark problems and the other was a real-world problem. Simulation results showed that CRO is very competitive with the few existing successful metaheuristics, having outperformed them in some cases, and CRO achieved the best performance in the real-world problem. Moreover, with the No-Free-Lunch theorem, CRO must have equal performance as the others on average, but it can outperform all other metaheuristics when matched to the right problem type. Therefore, it provides a new approach for solving optimization problems. CRO may potentially solve those problems which may not be solvable with the few generally acknowledged approaches. View full abstract»

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  • Performance of the (\mu /\mu _{I},\lambda )\hbox {-}\sigma {\rm SA} -ES on a Class of PDQFs

    Page(s): 400 - 418
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1757 KB) |  | HTML iconHTML  

    This paper investigates the behavior of (μ/μI,λ)-σSA-ES on a class of positive definite quadratic forms. After introducing the fitness environment and the strategy, the self-adaptation mechanism is analyzed with the help of the self-adaptation response function. Afterward, the steady state of the strategy is analyzed. The dynamical equations for the expectation of the mutation strength σ and the localization parameter ζ will be derived. Building on that, the progress rate ϕ is analyzed and tuned by means of the learning parameter τ. An approximate formula for τopt, yielding locally maximal progress, is presented. Finally, the performance of the σSA-rule is compared with the performance of the cumulative step size adaptation rule, and a rough approximation for the expected runtime is presented. View full abstract»

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  • Discovering Unique, Low-Energy Pure Water Isomers: Memetic Exploration, Optimization, and Landscape Analysis

    Page(s): 419 - 437
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1695 KB) |  | HTML iconHTML  

    The discovery of low-energy stable and meta-stable molecular structures remains an important and unsolved problem in search and optimization. In this paper, we contribute two stochastic algorithms, the archiving molecular memetic algorithm (AMMA) and the archiving basin hopping algorithm (ABHA) for sampling low-energy isomers on the landscapes of pure water clusters (H2O)n. We applied our methods to two sophisticated empirical water cluster models, TTM2.1-F and OSS2, and generated archives of low-energy water isomers (H2O)n n=3-15. Our algorithms not only reproduced previously-found best minima, but also discovered new global minima candidates for sizes 9-15 on OSS2. Further numerical results show that AMMA and ABHA outperformed a baseline stochastic multistart local search algorithm in terms of convergence and isomer archival. Noting a performance differential between TTM2.1-F and OSS2, we analyzed both model landscapes to reveal that the global and local correlation properties of the empirical models differ significantly. In particular, the OSS2 landscape was less correlated and hence, more difficult to explore and optimize. Guided by our landscape analyses, we proposed and demonstrated the effectiveness of a hybrid local search algorithm, which significantly improved the sampling performance of AMMA on the larger OSS2 landscapes. Although applied to pure water clusters in this paper, AMMA and ABHA can be easily modified for subsequent studies in computational chemistry and biology. Moreover, the landscape analyses conducted in this paper can be replicated for other molecular systems to uncover landscape properties and provide insights to both physical chemists and evolutionary algorithmists. View full abstract»

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  • A Dual-System Variable-Grain Cooperative Coevolutionary Algorithm: Satellite-Module Layout Design

    Page(s): 438 - 455
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (961 KB) |  | HTML iconHTML  

    The layout design of complex engineering systems (such as satellite-module layout design) is very difficult to solve in polynomial time. This is not only a complex coupled system design problem but also a special combinatorial problem. The fitness function for this problem is characterized as multimodal because of interference constraints among layout components (objects), etc. This characteristic can easily result in premature convergence when solving this problem using evolutionary algorithms. To deal with the above two problems simultaneously, we propose a dual-system framework based on the cooperative coevolutionary algorithm (CCEA, e.g., cooperative coevolutionary genetic algorithm) like multidisciplinary design optimization. The proposed algorithm has the characteristic of solving the complex coupled system problem, increasing the diversity of population, and decreasing the premature convergence. The basis for the proposed algorithm is as follows. The original coupled system P is decomposed into several subsystems according to its physical structure. The system P is duplicated as systems A and B, respectively. The A system is solved on a global level (all-in-one), whereas the solving of B system is realized through the computation of its subsystems in parallel. The individual migration between A and B is implemented through the individual migration between their corresponding subsystems. To reduce the computational complexity produced additionally by the dual-systems A and B, we employ a variable-grain model of design variables. During the process of optimization, the two systems A and B gradually approximate to the original system P, respectively. The above-proposed algorithm is called the dual-system variable-grain cooperative coevolution algorithm (DVGCCEA) or Oboe-CCEA. The numerical experimental results of a simplified satellite-module layout design case show that the proposed algorithm can obtain better robustness and trade-off between computational prec- - ision and computational efficiency. View full abstract»

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  • Expensive Multiobjective Optimization by MOEA/D With Gaussian Process Model

    Page(s): 456 - 474
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1471 KB) |  | HTML iconHTML  

    In some expensive multiobjective optimization problems (MOPs), several function evaluations can be carried out in a batch way. Therefore, it is very desirable to develop methods which can generate multipler test points simultaneously. This paper proposes such a method, called MOEA/D-EGO, for dealing with expensive multiobjective optimization. MOEA/D-EGO decomposes an MOP in question into a number of single-objective optimization subproblems. A predictive model is built for each subproblem based on the points evaluated so far. Effort has been made to reduce the overhead for modeling and to improve the prediction quality. At each generation, MOEA/D is used for maximizing the expected improvement metric values of all the subproblems, and then several test points are selected for evaluation. Extensive experimental studies have been carried out to investigate the ability of the proposed algorithm. View full abstract»

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  • IEEE Computational Intelligence Society Information

    Page(s): C3
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  • IEEE Transactions on Evolutionary Computation Information for authors

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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