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Evolutionary Computation, IEEE Transactions on

Issue 5 • Date Oct. 2002

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Displaying Results 1 - 9 of 9
  • In memoriam Alex S. Fraser [1923-2002]

    Page(s): 429 - 430
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    Freely Available from IEEE
  • Multi-objective optimization using evolutionary algorithms [Book Review]

    Page(s): 526
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    Freely Available from IEEE
  • Parallelism and evolutionary algorithms

    Page(s): 443 - 462
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (366 KB) |  | HTML iconHTML  

    This paper contains a modern vision of the parallelization techniques used for evolutionary algorithms (EAs). The work is motivated by two fundamental facts: 1) the different families of EAs have naturally converged in the last decade while parallel EAs (PEAs) are still lack of unified studies; and 2) there is a large number of improvements in these algorithms and in their parallelization that raise the need for a comprehensive survey. We stress the differences between the EA model and its parallel implementation throughout the paper. We discuss the advantages and drawbacks of PEAs. Also, successful applications are mentioned and open problems are identified. We propose potential solutions to these problems and classify the different ways in which recent results in theory and practice are helping to solve them. Finally, we provide a highly structured background relating to PEAs in order to make researchers aware of the benefits of decentralizing and parallelizing an EA View full abstract»

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  • A framework for evolutionary optimization with approximate fitness functions

    Page(s): 481 - 494
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    It is not unusual that an approximate model is needed for fitness evaluation in evolutionary computation. In this case, the convergence properties of the evolutionary algorithm are unclear due to the approximation error of the model. In this paper, extensive empirical studies are carried out to investigate the convergence properties of an evolution strategy using an approximate fitness function on two benchmark problems. It is found that incorrect convergence will occur if the approximate model has false optima. To address this problem, individual- and generation-based evolution control are introduced and the resulting effects on the convergence properties are presented. A framework for managing approximate models in generation-based evolution control is proposed. This framework is well suited for parallel evolutionary optimization, which is able to guarantee the correct convergence of the evolutionary algorithm, as well as to reduce the computation cost as much as possible. Control of the evolution and updating of the approximate models are based on the estimated fidelity of the approximate model. Numerical results are presented for three test problems and for an aerodynamic design example View full abstract»

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  • An evolutionary algorithm for resource-constrained project scheduling

    Page(s): 512 - 518
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (256 KB) |  | HTML iconHTML  

    The single-mode, single-project, resource-constrained project-scheduling problem is solved by an evolutionary algorithm. The design of this algorithm is presented. Results of a computational study on two sets of benchmark problems, the first consisting of 330 problem instances and the second 2040, are presented. These results show that the proposed algorithm is effective in terms of the number of times it achieves both the best-known solutions and the average error with respect to these solutions, particularly given that the best-known solutions have been compiled from various sources, using a variety of algorithms. Moreover, the computation time requirements are quite modest View full abstract»

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  • NOW G-Net: learning classification programs on networks of workstations

    Page(s): 463 - 480
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (413 KB) |  | HTML iconHTML  

    The automatic construction of classifiers (programs able to correctly classify data collected from the real world) is one of the major problems in pattern recognition and in a wide area related to artificial intelligence, including data mining. We present G-Net, a distributed evolutionary algorithm able to infer classifiers from precollected data. The main features of the system include robustness with respect to parameter settings, use of the minimum description length criterion coupled with a stochastic search bias, coevolution as a high-level control strategy, ability to face problems requiring structured representation languages, and suitability to parallel implementation on a network of workstations (NOW). Its parallel version, NOW G-Net, also described in this paper, is able to profitably exploit the computing power delivered by these platforms by incorporating a set of dynamic load distribution techniques that allow it to adapt to the variations of computing power arising typically in these systems. A proof-of-concept implementation is used in this paper to demonstrate the effectiveness of NOW G-Net on a variety of datasets View full abstract»

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  • From an individual to a population: an analysis of the first hitting time of population-based evolutionary algorithms

    Page(s): 495 - 511
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (574 KB) |  | HTML iconHTML  

    Almost all analyses of time complexity of evolutionary algorithms (EAs) have been conducted for (1 + 1) EAs only. Theoretical results on the average computation time of population-based EAs are few. However, the vast majority of applications of EAs use a population size that is greater than one. The use of population has been regarded as one of the key features of EAs. It is important to understand in depth what the real utility of population is in terms of the time complexity of EAs, when EAs are applied to combinatorial optimization problems. This paper compares (1 + 1) EAs and (N + N) EAs theoretically by deriving their first hitting time on the same problems. It is shown that a population can have a drastic impact on an EA's average computation time, changing an exponential time to a polynomial time (in the input size) in some cases. It is also shown that the first hitting probability can be improved by introducing a population. However, the results presented in this paper do not imply that population-based EAs will always be better than (1 + 1) EAs for all possible problems View full abstract»

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  • Genetic-fuzzy approach to the Boolean satisfiability problem

    Page(s): 519 - 525
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    This study is concerned with the Boolean satisfiability (SAT) problem and its solution in setting a hybrid computational intelligence environment of genetic and fuzzy computing. In this framework, fuzzy sets realize an embedding principle meaning that original two-valued (Boolean) functions under investigation are extended to their continuous counterparts resulting in the form of fuzzy (multivalued) functions. In the sequel, the SAT problem is reformulated for the fuzzy functions and solved using a genetic algorithm (GA). It is shown that a GA, especially its recursive version, is an efficient tool for handling multivariable SAT problems. Thorough experiments revealed that the recursive version of the GA can solve SAT problems with more than 1000 variables View full abstract»

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  • Genetic programming and evolutionary generalization

    Page(s): 431 - 442
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    In genetic programming (GP), learning problems can be classified broadly into two types: those using data sets, as in supervised learning, and those using an environment as a source of feedback. An increasing amount of research has concentrated on the robustness or generalization ability of the programs evolved using GP. While some of the researchers report on the brittleness of the solutions evolved, others proposed methods of promoting robustness/generalization. It is important that these methods are not ad hoc and are applicable to other experimental setups. In this paper, learning concepts from traditional machine learning and a brief review of research on generalization in GP are presented. The paper also identifies problems with brittleness of solutions produced by GP and suggests a method for promoting robustness/generalization of the solutions in simulating learning behaviors using GP View full abstract»

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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