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Hybrid Intelligent Systems, 2006. HIS '06. Sixth International Conference on

Date 13-15 Dec. 2006

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Displaying Results 1 - 25 of 78
  • Sixth International Conference on Hybrid Intelligent Systems - Cover

    Publication Year: 2006 , Page(s): c1
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  • Sixth International Conference on Hybrid Intelligent Systems-Title

    Publication Year: 2006 , Page(s): i
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  • Sixth International Conference on Hybrid Intelligent Systems-Copyright

    Publication Year: 2006 , Page(s): iv
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  • Sixth International Conference on Hybrid Intelligent Systems - TOC

    Publication Year: 2006 , Page(s): v - x
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  • Welcome from the Organizing Chairs

    Publication Year: 2006 , Page(s): xi
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  • Conference Committees

    Publication Year: 2006 , Page(s): xii
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  • Is That Possible? (Or is it Probable?)

    Publication Year: 2006 , Page(s): 1
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    In settings where we have significant amounts of data, the probability of events occurring can be calculated. But life is not like that. Human beings must cope with situations where we encounter only a small number of events, handle ambiguous and imprecise information, and respond correctly. Also, arguably, possibility values are more relevant to human beings in the understanding of risk and danger than probability values of unlikely events. In this work I describe some work in transforming low occurrence counts into estimation of imprecise probability. View full abstract»

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  • Status and Perspective of Intelligent System Design Automation

    Publication Year: 2006 , Page(s): 2
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    Intelligent systems have become both ubiquitous and more and more powerful and complex in the last decade. They base on an increasing variety of sensorial input, hybrid algorithms, and integrated, electronic embodiment. In particular, with the rapid advance of wireless and low-power implementation, new application fields, such as sensor networks or ambient intelligence gain momentum. While efficient design techniques are well established for microtechnique and -electronic system realization, the heart of an intelligent system, i.e., the structure from sensorial input to decision making, is still predominantly assembled in an expert driven, manual way. This represents a tedious, labor-intensive approach, which tends to end in locally optimal solutions due to the restricted covering of the potential search space implied by the problem and available sensors and algorithms. Consequently, the challenge of automated intelligent system design has been picked up exploiting obvious techniques from machine learning, i.e., neural networks, and more general optimization techniques based on appropriate assessment and optimization. The latter approach in particular is applied together with classical and advanced multi-dimensional signal processing. The talk will summarize important work contributed to that emerging field. The importance of hybrid approaches, including evolutionary optimization techniques, for effective and efficient, feasible system designs under embedding/integration constraints, e.g., power dissipation, will be pointed out. Then a proprietary approach and design methodology developed in the last nine years will be presented and demonstrated by examples from vision and other sensorial application systems. In addition to the obvious rapid-prototyping benefit, this approach and the underlying learning architecture together with appropriate hardware platforms offers improved robustness and fault-tolerance. View full abstract»

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  • Light-Weight Evolutionary Computation for Complex Image-Processing Applications

    Publication Year: 2006 , Page(s): 3
    Cited by:  Papers (1)
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    The expedience of today's image-processing applications is not any longer based on the performance of a single algorithm alone. These systems appear to be complex frameworks with a lot of subtasks that are solved by specific algorithms, adaptation procedures, data handling, scheduling, and parameter choices. The venture of using computational intelligence (CI) in such a context, thus, is not a matter of a single approach. Among the great choice of techniques to inject CI in an image-processing framework, the primary focus of this talk will be on the usage of so-called Tiny-GAs. This stands for an evolutionary procedure with low efforts, i.e. small population size (like 10 individuals), little number of generations, and a simple fitness. Obviously, this is not suitable for solving highly complex optimization tasks, but the primary interest here is not the best individuals' fitness, but the fortune of the algorithm and its population, which has just escaped the Monte-Carlo domain after random initialization. That this approach can work in practice will be demonstrated by means of selected image-processing applications, especially in the context of linear regression and line fitting; evolutionary post processing of various clustering results, in order to select a most suitable one by similarity; classification by the fitness values obtained after a few generations as well as segmentation of the main-color region. View full abstract»

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  • Monitoring Genetic Variations in Variable Length Evolutionary Algorithms

    Publication Year: 2006 , Page(s): 4
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (218 KB) |  | HTML iconHTML  

    Initially, Artificial Evolution focuses on Evolutionary Algorithms handling solutions coded in fixed length structures. In this context, the role of crossover is clearly the mixing of information between solutions. The development of Evolutionary Algorithms operating on structures with variable length, of which genetic programming is one of the most representative instances, opens new questions on the effects of crossover. Beside mixing, two new effects are identified : the diffusion of information inside solutions and the variation of the solutions sizes. In this paper, we propose a experimental framework to study these three effects and apply it on three different crossovers for genetic programming : the Standard Crossover, the One-Point Crossover and the Maximum Homologous Crossover. Exceedingly different behaviors are reported leading us to consider the necessary future decoupling of the mixing, the diffusion and the size variation. View full abstract»

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  • Particle Swarm Optimization of Feed-Forward Neural Networks with Weight Decay

    Publication Year: 2006 , Page(s): 5
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (219 KB) |  | HTML iconHTML  

    Training neural networks is a complex task of great importance in problems of supervised learning. In this work we analyze the use of the Particle Swarm Optimization algorithm and the cooperative variant with the weight decay mechanism for neural network training aiming better generalization performances. For evaluating these algorithms we apply them to benchmark classification problems of the medical field. The results showed that the weight decay mechanism implemented improved the mean generalization control of the two algorithms in all the tested problems. View full abstract»

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  • Learning Neural Networks for Visual Servoing Using Evolutionary Methods

    Publication Year: 2006 , Page(s): 6
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (154 KB) |  | HTML iconHTML  

    In this article we introduce a method to learn neural networks that solve a visual servoing task. Our method, called EANT, Evolutionary Acquisition of Neural Topologies, starts from a minimal network structure and gradually develops it further using evolutionary reinforcement learning. We have improved EANT by combining it with an optimisation technique called CMA-ES, Covariance Matrix Adaptation Evolution Strategy. Results from experiments with a 3 DOF visual servoing task show that the new CMAES based EANT develops very good networks for visual servoing. Their performance is significantly better than those developed by the original EANT and traditional visual servoing approaches. View full abstract»

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  • Emergence of Information Processor Using Real World--Real-Time Learning of Pursuit Problem

    Publication Year: 2006 , Page(s): 7
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (343 KB) |  | HTML iconHTML  

    Real-time reinforcement learning is difficult because number of trials is too much to complete learning within limited time. To solve the problem, we consider reduction of action-state space by information processor using real world without prior knowledge. We obtain the information processor in evolution by setting the fitness as ease of learning. As a typical example, we address pursuit problem in which dynamics is regarded. As a result, the processor has been obtained in evolution and agent has learned in real-time. View full abstract»

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  • On Basic Principles of Intelligent Systems Design

    Publication Year: 2006 , Page(s): 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (192 KB) |  | HTML iconHTML  

    In the paper we present results of computational experiments to suggest the possibility of a general optimality condition of complex systems: a system demonstrates the optimal performance for a problem, when the structural complexity of the system is in a certain relationship with the structural complexity of the problem. The optimality condition could be used as a basic principle in the design of intelligent systems optimizing their performance in the dynamic environment. View full abstract»

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  • A Comparative Analysis of Data Distribution Methods in an Agent-Based Neural System for Classification Tasks

    Publication Year: 2006 , Page(s): 9
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (188 KB) |  | HTML iconHTML  

    The NeurAge (Neural agents) system has been proposed as an alternative to transform the centralized decision making process of a multi-classifier system into a distributed, flexible and incremental one. This system has presented good results in some conventional (centralized) classification tasks. Nevertheless, in some classification tasks, relevant features might be distributed over a set of agent. These applications can be classified as distributed classification tasks. In this paper, a comparative investigation of the NeurAge system using some methods for data distribution will be performed. In addition, the performance of the NeurAge system will be compared with some existing multi-classifier systems. View full abstract»

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  • Stochastic Differential Portfolio Games with Regime Switching Model

    Publication Year: 2006 , Page(s): 10
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (195 KB) |  | HTML iconHTML  

    Stochastic dynamic investment games with regime switching model in continuous time between two investors are developed. The market coefficients are modulated by continuous-time Markov chain. There is a single payoff function which depends on both investors¿ wealth processes. One player chooses a dynamic portfolio strategy in order to maximize this expected payoff, while his opponent is simultaneously choosing a dynamic portfolio strategy so as to minimize the same quantity. A general result in optimal control for a stochastic differential game with a general payoff function is presented under some regular conditions. Use this general result to utility-based games of fixed duration, the optimal strategies and value of the games are derived explicitly. View full abstract»

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  • GNeurAge: An Evolutionary Agent-Based System for Classification Tasks

    Publication Year: 2006 , Page(s): 11
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (103 KB) |  | HTML iconHTML  

    The use of intelligent agents in the structure of multiclassifier systems has been investigated in order to overcome some drawbacks of these systems and, as a consequence, to improve the performance of such systems. As a result of this, the NeurAge system was proposed. This system has presented good results in some centralized and distributed classification tasks. In this paper, an investigation of using evolutionary techniques in the functioning of the NeurAge (GNeurAge) is performed. In order to do this, we are going to use genetic algorithm in two different phases: in the choice of the initial classifier; and during the functioning of NeurAge (test phase). View full abstract»

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  • Extracting Symbolic Rules from Clustering of Gene Expression Data

    Publication Year: 2006 , Page(s): 12
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (161 KB) |  | HTML iconHTML  

    In the last few years, the increasing automation applied to Biology processes has led to a fast accumulation of im- portant biological data. The wide biological implications present in these data makes its analysis unsuitable via con- ventional computing. In this context, Machine Learning (ML) techniques have been showing very promising. One of the ML techniques for analyzing these data is cluster- ing methods. Experimental studies have shown that, often, clusters generated via such methods are biologically mean- ingful. However, in general, the interpretation of the bio- logical meaning of the clusters formed is a very complex task. Thus, this paper invests its efforts in the study of tech- niques that makes the interpretation of clusters formed by clustering techniques more straightforward. In order to do so, unsupervisedML techniques (clustering techniques) will be associated with supervised ML techniques (rule genera- tion). The goal is to generate symbolic structures, such as IF-THEN rules, which are more comprehensible for humans View full abstract»

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  • Brain-Gene Ontology: Integrating Bioinformatics and Neuroinformatics Data, Information and Knowledge to Enable Discoveries

    Publication Year: 2006 , Page(s): 13
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (818 KB) |  | HTML iconHTML  

    The paper presents some preliminary results on the brain-gene ontology (BGO) project that is concerned with the collection, presentation and use of knowledge in the form of ontology. BGO includes various concepts, facts, data, software simulators, graphs, videos, animations, and other information forms, related to brain functions, brain diseases, their genetic basis and the relationship between all of them. The first version of the brain-gene ontology has been completed as a hierarchical structure and as an initial implementation in the Prot¿g¿ ontology building environment. View full abstract»

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  • A Novel Microarray Gene Selection Method Based on Consistency

    Publication Year: 2006 , Page(s): 14
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (95 KB) |  | HTML iconHTML  

    Consistency modeling for gene selection is a new topic emerging from recent cancer bioinformatics research. The result of classification or clustering on a training set was often found very different from the same operations on a testing set. Here, we address this issue as a consistency problem. We propose a new concept of performance-based consistency and a new novel gene selection method, Genetic Algorithm Gene Selection method in terms of consistency (GAGSc). The proposed consistency concept and GAGSc method were investigated on eight benchmark microarray and proteomic datasets. The experimental results show that the different microarray datasets have different consistency characteristics, and that better consistency can lead to an unbiased and reproducible outcome with good disease prediction accuracy. More importantly, GAGSc has demonstrated that gene selection, with the proposed consistency measurement, is able to enhance the reproducibility in microarray diagnosis experiments. View full abstract»

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  • Combining Greedy Method and Genetic Algorithm to Identify Transcription Factor Binding Sites

    Publication Year: 2006 , Page(s): 15
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (200 KB) |  | HTML iconHTML  

    Identification of Transcription Factor Binding Sites (TFBS) from the upstream region of genes remains a highly important and unsolved problem particularly in higher eukaryotic genomes. In this paper, we propose a novel approach to identify transcription factor binding sites. This approach combines greedy method and genetic algorithm (CGGA) to search conserved segment in the given sequence set. A new greedy method which can efficiently search a local optimal result is proposed. In order to solve the high complexity of this algorithm, we also give an effective improvement for this method. Then, we describe how to combine genetic algorithm with this greedy method to find the more optimal results. Greedy method is combined to the fitness function of the genetic algorithm. We apply this approach on two different TFBS sets and the results show that it can find correct result both effective and efficient, and for CRP binding sites, it get a more accurate result than Gibbs Sampler, AlignACE and MDGA. View full abstract»

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  • Investigation of a New Artificial Immune System Model Applied to Pattern Recognition

    Publication Year: 2006 , Page(s): 16
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (514 KB) |  | HTML iconHTML  

    The discovery of new functionalities through the study of human physiology has contributed toward the evolution of Artificial Immune Systems. The present work investigates a new architecture through observations of natural immunological behavior, for which application to known algorithms contributed toward an improved performance. This paper considers a boarding where the antibodies are grouped in an organized way and from an evolutionary process the antibodies that belong to these groupings can improve the adaptive immune reply to a determined antigen. Thus, antibodies of the same class are in the same grouping. Others techniques were implemented such as Clonalg, MLP and K-NN to compare this new model. View full abstract»

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  • RLM: A New Method of Encoding Weights in DNA Strands

    Publication Year: 2006 , Page(s): 17
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    How to encode weights in DNA computing is an important but challenging problem because many practical applications in the real world involve weights. In order to efficiently encode weights in DNA strands, we firstly proposed two definitions, the order number of weight and the relative length graph. And then, by means of the two definitions, we have devised a new method of encoding weights in DNA strands for a weighted graph G=(V,E,W), referred to relative length method (RLM). The RLM method can directly deal with weights of either real numbers or integers, even very small and very big positive weights, and the solution obtained in the RLM method isn¿t proportional to the length of DNA strand. The RLM method was applied to solve the traveling salesman problem, and it can be expanded to solve other numerical optimization problems. View full abstract»

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  • DNA Computing Model for the 0/1 Knapsack Problem

    Publication Year: 2006 , Page(s): 18
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (178 KB) |  | HTML iconHTML  

    We have devised a DNA encoding method and a corresponding DNA algorithm for the 0/1 knapsack problem. Suppose that item set I={1,2 ... n}, profit set P={p_1,p_2,...,p_n}, weight set W={w_1,w_2,...,w_n}, and knapsack capacity is c. We use two DNA strands s_i1 and s_i2 to encode each item i, where the DNA strand s_i1 is with a length of wi whose center part is with a length of p_i, and the DNA strand s_i2 is the reverse complement of the center part of s_i1. For any two items i,j we add one DNA strand s_aij as an additional code, which is the reverse complement of the last part of s_i1 and the first part of s_j1. The proposed DNA encoding method is an improvement on the previous ones, and it provides further evidence for the ability of DNA computing to solve numerical optimization problems. View full abstract»

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  • A Contribution to Automatic Design of Image Processing Systems--Breeding Optimized Non-Linear and Oriented Kernels for Texture Analysis

    Publication Year: 2006 , Page(s): 19
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (666 KB) |  | HTML iconHTML  

    The rapid development in image processing technology allows the tackling of application of increasing complexity. For efficient design of application specific systems design automation techniques are required. This paper reports on activities for texture classification employing non-linear oriented kernels configured by evolutionary optimization techniques. Our approach was tested with benchmark and application data from leather inspection and found viable and competitive in both cases. View full abstract»

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