2005 IEEE Congress on Evolutionary Computation

2-5 Sept. 2005

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  • [Cover]

    Publication Year: 2005, Page(s): C1
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  • The 2005 IEEE Congress on Evolutionary Computation

    Publication Year: 2005, Page(s): nil2
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  • Copyright page

    Publication Year: 2005, Page(s): nil3
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  • Preface

    Publication Year: 2005, Page(s): i
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  • 2005 Committee

    Publication Year: 2005, Page(s):ii - iii
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  • Review Committee

    Publication Year: 2005, Page(s):iii - v
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  • Table of contents

    Publication Year: 2005, Page(s):vii - xxiv
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  • Hybrid evolutionary algorithms for constraint satisfaction problems: memetic overkill?

    Publication Year: 2005, Page(s):1922 - 1928 Vol. 3
    Cited by:  Papers (6)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (1386 KB) | HTML iconHTML

    We study a selected group of hybrid EAs for solving CSPs, consisting of the best performing EAs from the literature. We investigate the contribution of the evolutionary component to their performance by comparing the hybrid EAs with their "de-evolutionarised" variants. The experiments show that "de-evolutionarising" can increase performance, in some cases doubling it. Considering that the problem ... View full abstract»

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  • Hybrid evolutionary static scheduling for heterogeneous systems

    Publication Year: 2005, Page(s):1929 - 1935 Vol. 3
    Cited by:  Papers (4)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (1686 KB) | HTML iconHTML

    The complexity of the static scheduling problem on heterogeneous resources has motivated the development of low complexity heuristics such as list scheduling. However, the greedy characteristic of such heuristics can, in many cases, generate poor results. This work proposes the integration of list scheduling heuristics with search mechanisms based on both genetic algorithms and GRASP, to efficient... View full abstract»

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  • Efficiently minimizing expensive cost functions with a hybrid evolutionary algorithm using clustering and a derivative-free optimizer: preliminary results

    Publication Year: 2005, Page(s):1937 - 1944 Vol. 3
    Cited by:  Papers (1)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (1700 KB) | HTML iconHTML

    A novel hybrid algorithm is presented to efficiently locate the global minimum of a function where each function evaluation is expensive and no expression is available for the function nor its derivatives. The hybrid employs an evolutionary algorithm, a density cluster analysis algorithm and a derivate-free optimizer in a multi-level hierarchical structure. The hybrid algorithm utilizes informatio... View full abstract»

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  • Cultural learning and diversity in a changing environment

    Publication Year: 2005, Page(s):1945 - 1950 Vol. 3
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    This paper examines the effect of cultural learning on a population of neural networks. We compare the genotypic and phenotypic diversity of populations employing only population learning and of populations using both population and cultural learning in a dynamic environment. We show that cultural learning is capable of achieving higher fitness levels and maintains a higher level of genotypic and ... View full abstract»

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  • Evolutionary strategies and genetic algorithms for dynamic parameter optimization of evolving fuzzy neural networks

    Publication Year: 2005, Page(s):1951 - 1958 Vol. 3
    Cited by:  Papers (3)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (5464 KB) | HTML iconHTML

    Evolving fuzzy neural networks are usually used to model evolving processes, which are developing and changing over time. This kind of network has some fixed parameters that usually depend on presented data. When data change over time, the best set of parameters also changes. This paper presents two approaches using evolutionary computation for the on-line optimization of these parameters. One of ... View full abstract»

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  • Adaptive cluster covering and evolutionary approach: comparison, differences and similarities

    Publication Year: 2005, Page(s):1959 - 1966 Vol. 3
    Cited by:  Papers (2)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (2899 KB) | HTML iconHTML

    In case the objective function to be minimized is not known analytically and no assumption can be made about the single extremum, global optimization (GO) methods must be used. Paper gives a brief overview of GO methods, with the special attention to principles of clustering, covering and evolution. Nine algorithms, including a simple GA, are compared in terms of effectiveness (accuracy), efficien... View full abstract»

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  • Optimization of an evolutionary algorithm for a tactile communication system

    Publication Year: 2005, Page(s):1967 - 1973 Vol. 3
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (4436 KB) | HTML iconHTML

    In this paper, we present an optimization method for a learning algorithm for generation of tactile stimuli which are adapted by means of tactile perception of a human. Because of special requirements for tactile perception tuning the optimization of the proposed learning algorithm cannot be performed basing on gradient-descent or likelihood estimation methods. Therefore, an automatic tactile clas... View full abstract»

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  • An improvement of database with local search mechanisms for genetic algorithms in large-scale computing environments

    Publication Year: 2005, Page(s):1974 - 1981 Vol. 3
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (5000 KB) | HTML iconHTML

    Recently, GA that uses large-scale computer systems comprised of massive processors has become feasible because of the emergence of super PC clusters and grid computation environments. Mechanisms to use massive computation resources laconically and to search effectively are necessary if large-scale computer systems are available. In this study, a new GA-specific database with the local search mech... View full abstract»

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  • A new estimation of distribution algorithm based on learning automata

    Publication Year: 2005, Page(s):1982 - 1987 Vol. 3
    Cited by:  Papers (4)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (1529 KB) | HTML iconHTML

    In this paper we introduce an estimation of distribution algorithm based on a team of learning automata. The proposed algorithm is a model based search optimization method that uses a team of learning automata as a probabilistic model of high quality solutions seen in the search process. Simulation results show that the proposed algorithm is a good candidate for solving optimization problems. View full abstract»

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  • A symbiosis algorithm for robotic control

    Publication Year: 2005, Page(s):1988 - 1995 Vol. 3
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    An algorithm inspired by the biological phenomenon of symbiosis is presented in this paper. The genetic diversity obtained from the creation of symbiotic relationships is investigated and the symbiosis algorithm is applied to a robotic forward kinematics control problem. Compared with other evolutionary optimisation techniques, the symbiosis algorithm is shown to be an effective paradigm in discov... View full abstract»

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  • Multiple bit encoding-based search algorithms

    Publication Year: 2005, Page(s):1996 - 2001 Vol. 3
    Cited by:  Papers (1)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (976 KB) | HTML iconHTML

    For a given real-world problem, we do not know a priori which representation suits this problem. Schnier and Yao showed the benefit of using the multiple real-coded evolutionary algorithm. This paper discusses the multiple bit encoding-based (standard binary and reflected Gray code) search algorithms. The population-based genetic algorithm and single individual-based random bit climber algorithm a... View full abstract»

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  • Evolving improved incremental learning schemes for neural network systems

    Publication Year: 2005, Page(s):2002 - 2009 Vol. 3
    Cited by:  Papers (8)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (3423 KB) | HTML iconHTML

    It is well known that incremental learning can often be difficult for traditional neural network systems, due to newly learned information interfering with previously learned information. In this paper, we present simulation results which demonstrate how evolutionary computation techniques can be used to generate neural network incremental learners that exhibit improved performance over existing s... View full abstract»

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  • A hybrid ant colony optimization approach (hACO) for constructing load-balanced clusters

    Publication Year: 2005, Page(s):2010 - 2017 Vol. 3
    Cited by:  Papers (3)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (2182 KB) | HTML iconHTML

    Nodes in an ad hoc network are usually organized into clusters, with each cluster being coordinated by a node acting as the cluster head (CH). Cluster members are one hop away from their CH. The collection of CHs give rise to a graph structure known as a dominating set. This paper proposes a hybrid ACO (hACO) approach that, when given a graph representing a network, selects a set of CHs in such a ... View full abstract»

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  • A simple hybrid evolutionary algorithm for finding Golomb rulers

    Publication Year: 2005, Page(s):2018 - 2023 Vol. 3
    Cited by:  Papers (4)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (1189 KB) | HTML iconHTML

    Finding Golomb rulers is an extremely challenging optimization problem (with many practical applications) that has been approached by a variety of search methods in recent years. This paper presents a hybrid evolutionary algorithm to find near-optimal Golomb rulers in reasonable time. The algorithm, which is conceptual simple and uses a natural modeling, focuses on feasibility, finding near-optima... View full abstract»

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  • Recombinative EMCMC algorithms

    Publication Year: 2005, Page(s):2024 - 2031 Vol. 3
    Cited by:  Papers (1)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (1845 KB) | HTML iconHTML

    Evolutionary Markov chain Monte Carlo (EMCMC) is a class of algorithms obtained by merging Markov chain Monte Carlo algorithms with evolutionary computation methods. EMCMC integrates techniques from the EC framework (population, recombination and selection) into the MCMC framework to increase the performance of the standard MCMC algorithms. In this paper, we show how to use recombination operators... View full abstract»

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  • XCS with computed prediction in continuous multistep environments

    Publication Year: 2005, Page(s):2032 - 2039 Vol. 3
    Cited by:  Papers (10)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (2709 KB) | HTML iconHTML

    We apply XCS with computed prediction (XCSF) to tackle multistep reinforcement learning problems involving continuous inputs. In essence we use XCSF as a method of generalized reinforcement learning. We show that in domains involving continuous inputs and delayed rewards XCSF can evolve compact populations of accurate maximally general classifiers which represent the optimal solution to the target... View full abstract»

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  • A memetic accuracy-based neural learning classifier system

    Publication Year: 2005, Page(s):2040 - 2045 Vol. 3
    Cited by:  Papers (1)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (1222 KB) | HTML iconHTML

    Learning classifier systems (LCS) traditionally use a binary string rule representation with wildcards added to allow for generalizations over the problem encoding. We have presented a neural network-based representation to aid their use in complex problem domains. Here each rule's condition and action are represented by a small neural network, evolved through the actions of the genetic algorithm.... View full abstract»

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  • Building anticipations in an accuracy-based learning classifier system by use of an artificial neural network

    Publication Year: 2005, Page(s):2046 - 2052 Vol. 3
    Cited by:  Papers (2)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (2099 KB) | HTML iconHTML

    Learning classifier systems which build anticipations of the expected states following their actions are a focus of current research. This paper presents a mechanism by which to create learning classifier systems of this type, here using accuracy-based fitness. In particular, we highlight the supervised learning nature of the anticipatory task and amend each rule of the system with a traditional a... View full abstract»

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