Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on

12-17 May 2002

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  • Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)

    Publication Year: 2002
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (1775 KB)

    The following topics were discussed:Evolutionary computation and biology; multiobjective evolutionary algorithms; evolutionary computation theory; molecular and quantum computing; combinatorial and numerical optimization; graphics and image processing; genome informatics; evolutionary intelligent agents; artificial immune systems; evolutionary multicriteria optimization; real-world applications an... View full abstract»

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  • CEC'02 author index

    Publication Year: 2002, Page(s):2035 - 2042
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    Freely Available from IEEE
  • Facial feature extraction using genetic algorithm

    Publication Year: 2002, Page(s):1895 - 1900
    Cited by:  Papers (12)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (742 KB) | HTML iconHTML

    An automatic facial feature extraction method is presented in this paper. The method is based on the edge density distribution of the image. In the preprocessing stage a face is approximated to an ellipse, and a genetic algorithm is applied to search for the best ellipse region match. In the feature extraction stage, the genetic algorithm is applied to extract the facial features, such as the eyes... View full abstract»

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  • Deterministic crowding, recombination and self-similarity

    Publication Year: 2002, Page(s):1516 - 1521
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (636 KB) | HTML iconHTML

    This paper proposes a new crossover operation named asymmetric two-point crossover (ATC). We show how deterministic crowding can be successful in the HIFF problem and the M7 function with this new crossover. We also point out that self-similarity in the solution plays an important role in the success of ATC View full abstract»

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  • A GA-based RBF classifier with class-dependent features

    Publication Year: 2002, Page(s):1890 - 1894
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (504 KB) | HTML iconHTML

    High dimensionality of data sets is a curse to classifiers. We propose to construct a novel radial basis function (RBF) classifier using class-dependent features by genetic algorithms (GA). Since each feature may have different capabilities in discriminating different classes, features should be masked differently for different classes. In our novel RBF classifier, each Gaussian kernel function of... View full abstract»

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  • And the winner is...Not the fittest [coevolution]

    Publication Year: 2002, Page(s):1510 - 1515
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (773 KB) | HTML iconHTML

    This paper carries out the study of natural networks (genetic, chemical and immune) evolving according to two levels of change called dynamics and metadynamics. The dynamics is the evolution in time of the concentration of the units currently present in the networks: the genetic, the molecular or the immune species. Their concentration evolves as a function of their network interaction with the ot... View full abstract»

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  • A novel evolutionary neural learning algorithm

    Publication Year: 2002, Page(s):1884 - 1889
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (513 KB) | HTML iconHTML

    We present a novel genetic algorithm and least square (GALS) based hybrid learning approach for the training of an artificial neural network (ANN). The approach combines evolutionary algorithms with matrix solution methods such as Gram-Schmidt, SVD, etc., to adjust weights for hidden and output layers. Our hybrid method (GALS) incorporates the evolutionary algorithm (EA) in the first layer and the... View full abstract»

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  • Evaluation of search performance of bacterial evolutionary algorithm

    Publication Year: 2002, Page(s):1343 - 1347
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (484 KB) | HTML iconHTML

    The search performance of evolutionary algorithms (EAs) has been widely studied. Interactions between genes in a chromosome, called "epistasis", make the theoretical investigation difficult. The goal of this study is a mathematical analysis of the effects of bacterial mutation on a bacterial evolutionary algorithm (BEA). The NK-landscape problem is employed for the investigation of this analysis i... View full abstract»

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  • Using a co-operative co-evolutionary genetic algorithm to solve optimal control problems in a hysteresis system

    Publication Year: 2002, Page(s):1504 - 1509
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (678 KB) | HTML iconHTML

    This paper presents the use of a co-operative co-evolutionary genetic algorithm (CCGA) for solving optimal control problems in a hysteresis system. The hysteresis system is a hybrid control system which can be described by a continuous multivalued state-space representation that can switch between two possible discrete modes. The problems investigated cover the optimal control of the hysteresis sy... View full abstract»

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  • Towards an evolutionary neural network for gait analysis

    Publication Year: 2002, Page(s):1922 - 1927
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (769 KB) | HTML iconHTML

    This paper presents initial investigations into an evolutionary neural network suitable for gait analysis of human motion. The approach here is to develop an intelligent black box that can take the physiological signals (EMG) and interpret them to give accurate information on the position and movement of the knee (gait). Two MLP networks are presented with weight evolving algorithms employing muta... View full abstract»

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  • Performance and population state metrics for rule-based learning systems

    Publication Year: 2002, Page(s):1781 - 1786
    Cited by:  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (623 KB) | HTML iconHTML

    We distinguish two types of metric for the evaluation of rule-based learning systems: performance metrics are derived from the feedback to the learning agent from its teacher or environment, while population state metrics are derived from inspection of the rule base used for decision making. We propose novel population state metrics for use with learning classifier systems, evaluate them using the... View full abstract»

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  • Don't push me! Collision-avoiding swarms

    Publication Year: 2002, Page(s):1691 - 1696
    Cited by:  Papers (40)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (581 KB) | HTML iconHTML

    This paper examines a particle swarm algorithm which has been applied to the generation of interactive, improvised music. An important feature of this algorithm is a balance between particle attraction to the centre of mass and repulsive, collision avoiding forces. These forces are not present in the classic particle swarm optimisation algorithms. A number of experiments illuminate the nature of t... View full abstract»

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  • Evolving the mapping between input neurons and multi-source imagery

    Publication Year: 2002, Page(s):1878 - 1883
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (630 KB) | HTML iconHTML

    We present a mutable input field concept that allows a neural network to evolve a mapping between its input layer and a 3-dimensional input cube consisting of a local window applied within multiple imagery sources, such as hyperspectral bands, feature maps, or even encoded tactical information regarding likely object location and class. This allows the net to exploit salient regions (both within a... View full abstract»

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  • Gene SPILL: an evolutionary algorithm based on bacterial gene exchange

    Publication Year: 2002, Page(s):1338 - 1342
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (439 KB) | HTML iconHTML

    Prokaryotes mainly reproduce by binary fission where the offspring are genetically identical to their parents. In order to take advantage of the benefits of sexual reproduction, these organisms have evolved ingenious ways to exchange genetic information. This article proposes an evolutionary algorithm called SPILL (Simulated Prokaryote Interchange of aLLeles) that is based on prokaryote genetic ex... View full abstract»

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  • Optimal initial structure of the RBF networks using time-frequency localization and genetic algorithm

    Publication Year: 2002, Page(s):1964 - 1969
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (532 KB) | HTML iconHTML

    In this paper, we propose the initial optimized structure of radial basis function networks that is simple and rapidly converges. We construct the hidden node with radial basis functions; their localization is similar to an approximation target function in the plane of time and frequency. We finally make a good decision for the initial structure for function approximation using a genetic algorithm View full abstract»

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  • Evolutionary optimization by distribution estimation with mixtures of factor analyzers

    Publication Year: 2002, Page(s):1396 - 1401
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (619 KB) | HTML iconHTML

    Evolutionary optimization algorithms based on the probability models have been studied to capture the relationship between variables in the given problems and finally to find the optimal solutions more efficiently. However, premature convergence to local optima still happens in these algorithms. Many researchers have used the multiple populations to prevent this ill behavior since the key point is... View full abstract»

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  • The force model: reducing the complexity by reformulating the problem

    Publication Year: 2002, Page(s):1498 - 1503
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (659 KB) | HTML iconHTML

    Most experiments in research on autonomous agents and mobile robots are performed either in simulation or on robots with static physical properties; evolvable hardware is hardly ever used. One of the very rare exceptions is the eyebot on which Lichtensteiger and Eggenberger have evolved simplified insect eyes. Even though substantially improved, the evolutionary models currently applied still lack... View full abstract»

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  • Rule extraction from an RBF classifier based on class-dependent features

    Publication Year: 2002, Page(s):1916 - 1921
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (559 KB) | HTML iconHTML

    Rule extraction is a technique for knowledge discovery. Compact rules with high accuracy are desirable. Due to the curse of irrelevant features to classifiers, feature selection techniques are discussed widely. We propose to extract rules based on class-dependent features from a radial basis function (RBF) classifier by genetic algorithms (GA). Each Gaussian kernel function of the RBF neural netwo... View full abstract»

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  • Studying the influence of synchronous and asynchronous parallel GP on programs length evolution

    Publication Year: 2002, Page(s):1727 - 1732
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (714 KB) | HTML iconHTML

    In this paper we present a study of parallel and distributed genetic programming models and their relationships with the bloat phenomenon. The experiments that we have performed have also allowed us to find an interesting link between the number of processes, subpopulations and the model we should use when applying parallelism to GP. We study the synchronous and asynchronous version of the island-... View full abstract»

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  • Hierarchical evolution of heterogeneous neural networks

    Publication Year: 2002, Page(s):1775 - 1780
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (790 KB) | HTML iconHTML

    This paper describes a hierarchical evolutionary technique developed to design and train feedforward neural networks with different activation functions on their hidden-layer neurons (heterogeneous neural networks). At the upper level, a genetic algorithm is used to determine the number of neurons in the hidden layer and the type of the activation function of those neurons. At the second level, ne... View full abstract»

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  • Solving numerical equations of hydraulic problems using particle swarm optimization

    Publication Year: 2002, Page(s):1688 - 1690
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (332 KB) | HTML iconHTML

    This paper describes how to solve numerical equations of hydraulic problems that involve the calculation of free and forced channels. The problem is modeled by using the Manning equation. This equation allows the calculation of outflows, inclination of the channels, roughness, loss of energy and other parameters that are a function of the geometry of the channels. Since this equation presents high... View full abstract»

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  • Evolving modular genetic regulatory networks

    Publication Year: 2002, Page(s):1872 - 1877
    Cited by:  Papers (33)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (738 KB) | HTML iconHTML

    We introduce a system that combines ontogenetic development and artificial evolution to automatically design robots in a physics-based, virtual environment. Through lesion experiments on the evolved agents, we demonstrate that the evolved genetic regulatory networks from successful evolutionary runs are more modular than those obtained from unsuccessful runs View full abstract»

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  • Linkage identification based on epistasis measures to realize efficient genetic algorithms

    Publication Year: 2002, Page(s):1332 - 1337
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (661 KB) | HTML iconHTML

    Genetic algorithms (GAs) process building blocks (BBs) mixed and tested through genetic recombination operators. To realize effective BB processing, linkage identification, which detects a set of tightly linked loci, is essential. This paper proposes linkage identification with epistasis measures (LIEM), which detects linkage groups based on a pair-wise epistasis measure View full abstract»

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  • A memetic algorithm instantiated with selection sort consistently finds global optima for the error-correcting graph isomorphism

    Publication Year: 2002, Page(s):1958 - 1963
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (677 KB) | HTML iconHTML

    We study several tested cases of the error-correcting graph isomorphism problem. The set Sn of n! permutations on n items is the search space for this optimization problem. We apply MA-sorting. This is a memetic algorithm with domain-independent mutation operators based on classical sorting. Each sorting algorithm works on results to a comparison predicate and defines a path in SI.,. In MA-sorting... View full abstract»

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  • Blocked stochastic sampling versus Estimation of Distribution Algorithms

    Publication Year: 2002, Page(s):1390 - 1395
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (633 KB) | HTML iconHTML

    The Boltzmann distribution is a good candidate for a search distribution for optimization problems. We compare two methods to approximate the Boltzmann distribution - Estimation of Distribution Algorithms (EDA) and Markov Chain Monte Carlo methods (MCMC). It turns out that in the space of binary functions even blocked MCMC methods outperform EDA on a small class of problems only. In these cases a ... View full abstract»

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