1,721,038 research outputs found
Rabnet: A Real-valued Antibody Network For Data Clustering
No full textThis paper proposes a novel constructive learning algorithm for a competitive neural network. The proposed algorithm is developed by taking ideas from the immune system and demonstrates robustness in the initial experiments reported here for a benchmark problem. Comparisons with results from the literature are also provided. To automatically segment the resultant neurons at the output, a tool from graph theory was used with promising results. General discussions and avenues for future works are also provided.371372Fritzke, B., Growing cell structures - A self-organizing network for unsupervised and supervised learning (1994) Neural Networks, 7 (9), pp. 1441-1460Zahn, C.T., Graph-theoretical methods for detecting and describing gestalt clusters (1971) IEEE on Comp., C-20, pp. 68-86De Castro, L.N., Von Zuben, F.J., De D Jr., G.A., The construction of a boolean competitive neural network using ideas from immunology (2003) Neurocomputing, 50 C, pp. 51-85De Castro, L.N., Von Zuben, F.J., Immune and neural network models: Theoretical and empirical comparisons (2001) International Journal of Computational Intelligence and Applications (IJCIA), 1 (3), pp. 239-257De Castro, L.N., Timmis, J., (2002) Artificial Immune Systems: A New Computational Intelligence Approach, , Springer-VerlagKasabov, N., (2002) Evolving Connectionist Systems: Methods and Applications in Bioinformatics, , Brain Study and Intelligent Machines, Springer VerlagHaykin, S., (1999) Neural Networks - A Comprehensive Foundation, , Prentice Hall, 2nd EdKohonen, T., (2000) Self-organizing Maps, , Springer-Verlag, 3rd E
Data Clustering With A Neuro-immune Network
No full textThis paper proposes a novel constructive learning algorithm for a competitive neural network. The proposed algorithm is developed by taking ideas from the immune system and demonstrates robustness for data clustering in the initial experiments reported here for three benchmark problems. Comparisons with results from the literature are also provided. To automatically segment the resultant neurons at the output, a tool from graph theory was used with promising results. A brief sensitivity analysis of the algorithm was performed in order to investigate the influence of the main user-defined parameters on the learning speed and accuracy of the results presented. General discussions and avenues for future works are also provided. © Springer-Verlag Berlin Heidelberg 2005.3610PART I12791288Cho, S.B., Self-organizing map with dynamical node splitting: Application to handwritten digit recognition (1997) Neural Computation, 9, pp. 1345-1355Dasgupta, D., Artificial neural networks and artificial immune systems: Similarities and differences (1997) Proc. of the IEEE SMC, 1, pp. 873-878De Castro, L.N., Von Zuben, F.J., (2004) Recent Developments in Biologically Inspired Computing, , Idea Group PublishingDe Castro, L.N., Von Zuben, F.J., Júnior, G.A.D.D., The construction of a boolean competitive neural network using ideas from immunology (2003) Neurocomputing, 50 C, pp. 51-85De Castro, L.N., Von Zuben, F.J., Immune and neural network models: Theoretical and empirical comparisons (2001) International Journal of Computational Intelligence and Applications (IJCIA), 1 (3), pp. 239-257De Castro, L.N., Timmis, J., (2002) Artificial Immune Systems: A New Computational Intelligence Approach, , Springer-VerlagHaykin, S., (1999) Neural Networks - A Comprehensive Foundation, , Prentice Hall, 2ndKasabov, N., Evolving connectionist systems: Methods and applications in bioinformatics (2002) Brain Study and Intelligent Machines, , Springer VerlagMarsland, J., Shapiro, S., Nehmzaw, U., A self-organising network that grows when required (2002) Neural Networks, 15, pp. 1041-1058Prim, R.C., Shortest connection networks and some generalizations (1957) Bell Sys. Tech. Journal, 36, pp. 1389-1401Ritter, J., Kohonen, T., Self-organizing semantic maps (1989) Biolog. Cybern., 61, pp. 241-254Segel, L., Perelson, A.S., Computations in shape space: A new approach to immune network theory (1988) Theoretical Immunology, 2, pp. 321-343. , ed. A. S. PerelsonUltsch, A., Knowledge extraction from artificial neural networks and applications (1993) Information and Classification, pp. 307-313. , O. Optiz et al. (Eds.), SpringerZahn, T., Graph-theoretical methods for detecting and describing gestalt clusters (1971) IEEE Trans. on Comp., C-20, pp. 68-8
Mlp-based Equalization And Pre-distortion Using An Artificial Immune Network
No full textDue to its universal approximation capability, the multilayer perceptron (MLP) neural network has been applied to several function approximation and classification tasks. Despite its success in solving these problems, its training, when performed by a gradient-based method, is sometimes hindered by the existence of unsatisfactory solutions (local minima). In order to overcome this difficulty, this paper proposes a novel approach to the training of a MLP based on a simple artificial immune network model. The application domain for assessing the performance of the proposed technique is that of digital communications, in particular, the problems of channel equalization and pre-distortion. The obtained simulation results demonstrate that the proposal is capable of efficiently solving the problems tackled. © 2005 IEEE.177182Doering, A., Galicki, M., Witte, H., Structure optimization of neural networks with the a*-algorithm (1997) IEEE Transactions on Neural Networks, 8 (6), pp. 1434-1445Yao, X., Evolutionary artificial neural networks (1995) Encyclopedia of Computer Science and Technology, 33, pp. 137-170. , A. Kent and J. G. Williams, editors, Marcel Dekker Inc., New YorkGudise, V.G., Venayagamoorthy, G.K., Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks (2003) Proceedings of the IEEE Swarm Intelligence Symposium 2003 (SIS 2003), pp. 110-117. , Indianapolis, Indiana, USADe Castro, L.N., Timmis, J.I., (2002) Artificial Immune Systems: A New Computational Intelligence Approach, , Springer-Verlag, LondonChen, S., Mulgrew, B., Grant, P.M., A clustering technique for digital communications channel equalization using radial basis function networks (1993) IEEE Trans, on Neural Networks, 4 (4), pp. 570-579Haykin, S., (1998) Neural Networks: A Comprehensive Foundation, , Prentice HallIbnkahla, M., Neural network predistortion technique for digital satellite communications (2000) Proceedings of ICASSP, 6, pp. 5-9. , JuneDe Castro, L.N., Timmis, J.I., Artificial immune systems as a novel soft computing paradigm (2003) Soft Computing Journal, 7 (8), pp. 526-544De Attux, R.R.F., Loiola, M.B., Suyama, R., De Castro, L.N., Von Zuben, F.J., Romano, J.M.T., Blind search for optimal wiener equalizers using an artificial immune network model (2003) EURASIP Journal of Applied Signal Processing, 2003 (8), pp. 740:747De Attux, R.R.F., De Castro, L.N., Von Zuben, F.J., Romano, J.M.T., A paradigm for blind IIR equalization using the constant modulus criterion and an artificial immune network (2003) Proceedings of the IEEE NNSP, , Toulouse, FranceDe Castro, L.N., Von Zuben, F.J., A hybrid paradigm for weight initialization in supervised feedforward neural network learning (1998) ICS-Workshop on Artificial Intelligence, pp. 30-37. , Tainan, Taiwan, DecemberBattiti, R., First- and second-order methods for learning: Between steepest descent and Newton's method (1992) Neural Computation, 4 (2), pp. 141-16
Heuristics To Avoid Redundant Solutions On Population-based Multimodal Continuous Optimization
No full textIn population-based meta-heuristics, the generation and maintenance of diversity seem to be crucial to deal with multimodal continuous optimization. However, usually this crucial aspect is not an inherent feature of generally adopted meta-heuristics. In this paper, we propose to associate diversity maintenance with the detection and elimination of redundant candidate solutions in the search space, more specifically candidate solutions located at the same attraction basin of a local optimum. Two low computational cost heuristics are proposed to detect redundancy, in a pairwise comparison of candidate solutions and by extracting local features of the fitness landscape at runtime. Those heuristics are not tied to a specific class of algorithms, and are thus able to be incorporated into a broad range of population-based meta-heuristics, and even into multiple executions of non-population-based algorithms. In a set of experimental results, the two heuristics were implemented as an attached module of an already existing multipopulation meta-heuristics, and the results indicate that they operate properly, no matter the number and conformation of the attraction basins in multimodal optimization problems. © 2011 IEEE.23212328Czogala, E., Zimmermann, H., Hans-Jurgen, Decision making in uncertain environments (1986) European Journal of Operational Research, 23 (2), pp. 202-212. , Elsevier, FebruaryDeb, K., (2001) Multi-objective Optimization Using Evolutionary Algorithms, , WileyJin, Y.J., Branke, Evolutionary optimization in uncertain environments - A survey (2005) IEEE Trans. on Evolutionary Computation, 9 (3)Pasti, R., De Castro, L.N., Bio-inspired and gradient-based algorithms to train MLPs: The influence of diversity (2009) Information Sciences, 179, pp. 1441-1453Alba, E., (2005) Parallel Metaheuristics: A New Class of Algorithms, , WileyGlover, F., Kochenberger, G.A., (2003) Handbook of Metaheuristics, , SpringerBazaraa, M.M., Sherali, H.D.H.D., Shetty, C.M.C.M., (1993) Nonlinear Programming - Theory and Algorithms, , 2° edição, John Wiley & Sons IncBäck, T.T., Fogel, D.B., Michalewicz, Z., Evolutionary computation 1 basic algorithms and operators (2000) Institute of Physiscs Publishing (IOP), Bristol and PhiladelphiaBäck, T.T., Fogel, D.B., Michalewicz, Z., (2000) Evolutionary Computation 2 Advanced Algorithms and Operators, , Institute of Physiscs Publishing IOP, Bristol and PhiladelphiaKennedy, J., Eberhart, R.R., Shi, Y., (2001) Swarm Intelligence, , Morgan Kauffman PublishersDe Castro, L.N., Timmis, J., (2002) Artificial Immune Systems: A New Computational Intelligence Approach, , Springer-VerlagAlba, E., Dorronsoro, B., The exploration/exploitation tradeoff in dynamic cellular genetic algorithms (2005) IEEE Transactions on Evolutionary Computation, 9 (2), pp. 126-142. , DOI 10.1109/TEVC.2005.843751Reyes-Sierra, M., Coello Coello, C.A., Multi-objective particle swarm optimizers: A survey of the state-of-the-art (2006) International Journal of Computational Intelligence Research, 2 (3), pp. 287-308Maia, R.D., Pasti, R.R., De Castro, L.N., A bio-inspired strategy to generate diversity in the particle swarm optimization algorithm (2009) First Workshop on Emergent Computing, , Santiago. Jornadas Chilenas de Computación, 2009De Castro, L.N., Von Zuben, F.J., Learning and optimization using the clonal selection principle (2002) IEEE Transactions on Evolutionary Computation, 6 (3), pp. 239-251. , DOI 10.1109/TEVC.2002.1011539, PII S1089778X02060654De Franca, F.O., Von Zuben, F.J., De Castro, L.N., An artificial immune network for multimodal function optimization on dynamic environments (2005) GECCO 2005 - Genetic and Evolutionary Computation Conference, pp. 289-296. , GECCO 2005 - Genetic and Evolutionary Computation Conferenc
A Hierarchical Immune Network Applied To Gene Expression Data
No full textThis paper describes a new proposal for gene expression data analysis. The method used is based on a hierarchical approach to a hybrid algorithm, which is composed of an artificial immune system, named aiNet, and a well known graph theoretic tool, the minimal spanning tree (MST). This algorithm has already proved to be efficient for clustering gene expression data, but its performance may decrease in some specific cases. However, through the use of a hierarchical approach of immune networks it is possible to improve the clustering capability of the hybrid algorithm, such that it becomes more efficient, even when the data set is complex. The proposed methodology is applied to the yeast data and gives important conclusions of the similarity relationships among genes within the data set. © Springer-Verlag 2004.32391427Bezerra, G.B., De Castro, L.N., Bioinformatics Data Analysis Using an Artificial Immune Network (2003) Lecture Notes in Computer Sciences, Proc. of Second International Conference, ICARIS 2003, pp. 22-33. , Edinburgh, UKDe Castro, L.N., Timmis, J.I., Hierarchy and Convergence of Immune Networks: Basic Ideas and Preliminary Results (2002) Proc. First International Conference, ICARIS 2002, pp. 231-240De Castro, L.N., Von Zuben, F.J., AiNet: An artificial Immune Network for Data Analysis (2001) Data Mining: A Heuristic Approach, pp. 231-259. , H. A. Abbass, R. A. Saker, and C. S. Newton (Eds.), Idea Group Publishing, USA, Chapter XIIDe Castro, L.N., Von Zuben, F.J., The Clonal Selection Algorithm with Engineering Applications (2000) GECCO'00 Proc. of the Genetic and Evolutionary Computation Conference - Workshop Proceedings, pp. 36-37De Castro, L.N., Von Zuben, F.J., An Evolutionary Immune Network for Data Clustering (2000) Proc. of IEEE SBRN - Brazilian Symposium on Neural Networks, pp. 84-89Eisen, M.B., Spellman, P.T., Brow, P.O., Botstein, D., Cluster Analysis and Display of Genome-wide Expression Patterns (1998) Proc. Natl. Acad. Sci, 95, pp. 14863-14868. , USAEveritt, B., Landau, S., Leese, M., (2001) Cluster Analysis, Fourth Edition, , Oxford University PressGomes, L.C.T., Von Zuben, F.J., Moscato, P.A., Ordering Microarray Gene Expression Data Using a Self-Organising Neural Network (2002) Proceedings of the 4th International Conference on Recent Advances in Soft Computing (RASC2002), pp. 307-312. , Nottingham, United Kingdom, DecemberHerrero, J., Valencia, A., Dopazo, A., A hierarchical unsupervised growing neural network for clustering gene expression patterns (2001) Bioinformatics, 17, pp. 126-136Herwig, R., Poustka, A.J., Mller, C., Bull, C., Lehrach, H., O'Brien, J., Large-scale clustering of cDNA-fingerprinting data (1999) Genome Res., 9, pp. 1093-1105Shulze, A., Downward, D.J., Navigating gene expression using Microarrays - A technology review (2001) Nature Cell Biology, 3, pp. E190-E195Xu, Y., Olman, V., Xu, D., Minimum Spanning Trees for Gene Expression Data Clustering (2002) Bioinformatics, 18, pp. 536-545Yeung, K.Y., (2001) Cluster Analysis of Gene Expression Data, , Ph.D. Thesis, Computer Science, University of Washington, Seattle, WA, USAZahn, C.T., Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters (1971) IEEE Trans. on Computers, C-20 (1), pp. 68-8
Bioinformatics Data Analysis Using An Artificial Immune Network
No full textThis work describes a new proposal for gene expression data clustering based on a combination of an immune network, named aiNet, and the minimal spanning tree (MST). The aiNet is an AIS inspired by the immune network theory. Its main role is to perform data compression and to identify portions of the input space representative of a given data set. The output of aiNet is a set of antibodies that represent the data set in a simplified way. The MST is then built on this network, and clusters are determined by using a new method for detecting the inconsistent edges of the tree. An important advantage of this technique over the classical approaches, like hierarchical clustering, is that there is no need of previous knowledge about the number of clusters and their distributions. The hybrid algorithm was first applied to a benchmark data set to demonstrate its validity, and its results were compared with those produced by other approaches from the literature. Using the full yeast S. cerevisiae gene expression data set, it was possible to detect a strong interconnection of the genes, hindering the perception of inconsistencies that may lead to the separation of data into clusters. © Springer-Verlag Berlin Heidelberg 2003.27872233Baldi, P., Brunak, S., (2001) Bioinfortnatics - The Machine Learning Approach, Second Edition, , MIT Press, Cambridge, MassachussettsBurnet, F.M., (1959) The Clonal Selection Theory of Acquired Immunity, , Cambridge University PressDe Castro, L.N., Von Zuben, F.J., aiNet: An artificial Immune Network for Data Analysis (2001) Data Mining: a Heuristic Approach, pp. 231-259. , H. A. Abbass, R. A. Saker, and C. S. Newton (Eds.), Idea Group Publishing, USA, Chapter XIIDe Castro, L.N., Von Zuben, F.J., The Clonal Selection Algorithm with Engineering Applications (2000) GECCO'00 Proc. of the Genetic and Evolutionary Computation Conference - Workshop Proceedings, pp. 36-37De Castro, L.N., Von Zuben, F.J., An Evolutionary Immune Network for Data Clustering (2000) Proc. of IEEE SBRN - Brazilian Symposium on Neural Networks, pp. 84-89Eisen, M.B., Spellman, P.T., Brow, P.O., Botstein, D., Cluster Analysis and Display of Genome-wide Expression Patterns (1998) Proc. Natl. Acad. Sci, 95, pp. 14863-14868. , USAEveritt, B., (1993) Cluster Analysis, , Heinemann Educational BooksFausset, L., (1994) Fundamentals of Neural Networks: Architectures, Algorithms and Applications, , Ed. Prentice-Hall, New Jersey, USAGomes, L.C.T., Von Zuben, F.J., Moscato, P., Ordering Gene Expression Data Using One-Dimensional Self-Organizing Maps (2002) Proc. of the 1st Brazilian Workshop on Bioinformatics, pp. 91-93. , Gramado, RS, BrazilHerwig, R., Poustka, A.J., Mller, C., Bull, C., Lehrach, H., O'Brien, J., Large-scale clustering of cDNA-fingerprinting data (1999) Genome Res., 9, pp. 1093-1105Jerne, N.K., Towards a Network Theory of the Immune System (1974) Ann. Immunol. (Inst. Pasteur), pp. 373-389Lockhart, D.J., Expression monitoring by hybridization to high-density oligonucleotide arrays (1996) Nature Biotechnology, 14, pp. 1675-1680Luscombe, N.M., Greenbaum, D., Gerstein, M., What is Bioinformatics? - A Proposed Definition and Overview of the Field (2001) Methods of Information in Medicine, 40, pp. 346-358Michalewicz, Z., (1999) Genetic Algorithms + Data Structures = Evolution Programs, Third Edition, , Ed. Springer, Nova York, USAPrim, R.C., Shortest Connection Networks and Some Generalizations (1957) Bell Sys. Tech. Journal, 36, pp. 1389-1401Schena, M., Parallel human genome analysis: Microarray-based expression monitoring of 1000 genes (1996) Proc. Natl. Acad. Sci. USA, 93, pp. 10614-10619Xu, Y., Olman, V., Xu, D., Minimum Spanning Trees for Gene Expression Data Clustering (2002) Bioinformatics, 18, pp. 536-545Yeung, K.Y., (2001) Cluster Analysis of Gene Expression Data, , Ph.D. Thesis, Computer Science, University of Washington, Seattle, WA, USAZahn, C.T., Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters (1971) IEEE Trans. on Computers, C-20 (1), pp. 68-8
An Immunological Density-preserving Approach To The Synthesis Of Rbf Neural Networks For Classification
No full textRadial Basis Function (RBF) neural networks are universal approximators and have been used for a wide range of applications. Aiming at reducing the number of neurons in the hidden layer, for regularization purposes, the center and dispersion of each RBF have to be properly defined by means of competitive learning. Only the output weights will be defined in a supervised manner. One of the drawbacks of such learning methodology, involving unsupervised and supervised learning, is that the centers will be defined so that regions in the input space with a high density of samples tend to be under-represented and those regions with a low density of samples tend to be over-represented. Additionally, few approaches provide a proper and individual indication of the dispersion of each RBF. This paper presents an immune density-preserving algorithm with adaptive radius, called ARIA, to determine the number of centers, their location and the dispersion of each RBF, based only on the available training data set. Considering classification problems, the algorithm to determine the hidden layer is compared to another immune-inspired algorithm called aiNet, K-means and the random choice of centers. The classification accuracy of the final network is compared to another density based approach and a decision tree classifier, C 5.0. The results are reported and analyzed. © 2006 IEEE.929935Hartman, E., Keeler, J., Kowalski, J., Layered neural networks with gaussian hidden units as universal approximations (1990) Neural Computation, 2, pp. 210-215Poggio, T., Girosi, F., (1989) A theory of networks for approximation and learning, , citeseer.ist.psu.edu/article/poggio89theory.html, MIT AI Lab, Tech. Rep. AIM-1140, Online, AvailableMoody, J.E., Darken, C.J., Fast learning in networks of locally tuned processing units (1989) Neural Computation, 1, pp. 281-294Hwang, Y.-S., Bang, S.-Y., An efficient method to construct a radial basis function neural network classifier (1997) Neural Network, 10 (9), pp. 1495-1503Girosi, F., Poggio, M., Poggio, T., Regularization theory and neural networks architectures (1995) Neural Computation, 7 (2), pp. 219-269Orr, M.J.L., Regularization in the selection of radial basis function centers (1995) Neural Computation, 7 (3), pp. 606-623de Castro, L.N., Zuben, F.J.V., Automatic determination of radial basis functions: An immunity-based approach (2001) Int. J. Neural Syst, 11 (6), pp. 523-535Lee, S.-J., Hou, C.-L., An ART-based construction of RBF networks (2002) IEEE-Neural Networks, 13, pp. 1308-1321. , NovLeonardis, A., Bischof, H., An efficient MDL-based construction of RBF networks (1998) Neural Networks, 11 (5), pp. 963-973Bezerra, G.B., Barra, T.V., de Castro, L.N., Zuben, F.J.V., Adaptive radius immune algorithm for data clustering (2005) International Conference on Anificial Immune Systems (ICARIS), pp. 290-303de Castro, L.N., Timmis, J., (2002) Artificial Immune Systems: A New Computational Intelligence Approach, , Springer-VerlagHaykin, S., (1999) Neural Networks: A Comprehensive Foundation, , Prentice Hall PTRCherkassky, V.S., Mulier, F., (1998) Learning from Data: Concepts, Theory, and Methods, , New York. NY, USA: John Wiley & Sons, IncChen, S., Cowan, C.F.N., Grant, P.M., Orthogonal least squares learning algorithm for radial basis function networks (1991) IEEE Trans. Neural Networks, 2 (2), pp. 302-309. , MarchBezerra, G.B., Barra, T.V., de Castro, L.N., Zuben, F.J.V., Handling data sparseness in gene network reconstruction (2005) ser. Computational Intelligence in Bioinformatics and Computational Biology, pp. 1-8. , Symposium on Computational Intelligence in Bioinformatics and Computational Biology, San DiegoAda, G.L., Nossal, S.G., The clonal-selection theory (1987) Scientific American, 257 (2), pp. 50-57Jerne, N.K., Towards a network theory of the immune system (1974) Ann. Immunol. (Inst. Pasteur) 125C, pp. 373-389Seber, G.A.F., (1984) Multivariate Observations, , WileyFoster, M.R., The New Science of Simplicity (2001) ser. Simplicity. Inference and Modelling, 1, pp. 83-117. , Cambridge University Press, chapter 5, ppWang, H., Bell, D.A., Düntsch, I., A density based approach to classification (2003) SAC, pp. 470-474Quinlan, (2003) C 5.0 data mining tool, , http://www.rulequest.com, Online, Availabl
Immune-inspired Dynamic Optimization For Blind Spatial Equalization In Undermodeled Channels
No full textIn this work, we propose an evolutionary-like approach to the problem of blind adaptive spatial filtering that is based on the decision-directed criterion and on the doptaiNet, an artificial immune network conceived to perform multimodal search in dynamic environments. The proposal was tested under static and time-varying undermodeled channel models, and, in all cases, its ability to find and track a solution close to the Wiener global optimum was attested. The obtained results reveal that the dopt-aiNet may decisively enhance the performance of adaptive arrays in scenarios built from elements that are representative of some aspects of real-world communication systems. © 2006 IEEE.28962903Frost III, O.L., An Algorithm for Linearly Contained Adaptive Array Processing (1972) Proceedings of the IEEE, 60, pp. 926-935. , AugApplebaum, S., Adaptive Arrays (1976) IEEE Trans. on Antennas and Propagation, AP-24 (5), pp. 585-598Haykin, S., (1996) Adaptive Filter Theory, , 3rd edition, Prentice HallPapadias, C.B., Paulraj, A., A constant modulus algorithm for multiuser separation in the presence of delay spread using antenna arrays (1997) IEEE Signal Processing Letters, 4 (6), pp. 171-181(1994) Blind Deconvolution, , S. Haykin ed, Prentice HallYacoub, M.D., (1993) Foundations of Mobile Radio Engineering, , CRC Pressde França, F.O., Von Zuben, F.J., de Castro, L.N., An Artificial Immune Network for Multimodal Function Optimization on Dynamic Environments (2005) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO, pp. 289-296Branke, J., (2001) Evolutionary Optimization in Dynamic Environments, , Kluwerde Castro, L.N., Timmis, J., (2002) Artificial Immune Systems: A New Computational Intelligence Approach, , Springer-Verlagde Castro, L.N., Timmis, J., An Artificial Immune Network for Multimodal Function Optimization (2002) Proc. of the IEEE Congress on Evolutionary Computation, 1, pp. 699-674de Castro, L.N., Von Zuben, F.J., Learning and Optimization Using the Clonal Selection Principle (2002) IEEE Transactions on Evolutionary Computation, 6 (3), pp. 239-251Gaspar, A., Collard, P., From GAs to Artificial Immune Systems: Improving Adaptation in Time Dependent Optimization (1999) Proc. of the IEEE Congress on Evolutionary Computation, pp. 1867-1874Bazaraa, M.S., Sherali, H.D., Shetty, C.M., (1993) Nonlinear Programming: Theory and Algorithms, , 2nd edition, Wile
A Proposal For Blind Fir Equalization Of Time-varying Channels
No full textThe multimodal and time-varying aspects of blind equalization problems in communication systems are treated here by means of an immune-inspired strategy capable of estimating the coefficients of the FIR equalization filter in an unsupervised manner. The associated optimization problem is solved by means of a population-based search technique characterized by a dynamic control of the population size and diversity maintenance. Static and time-varying channels have been proposed in simulated scenarios, aiming at indicating the tracking capability derived from the adaptive adjustment of the coefficients of the blind equalizer. © 2005 IEEE.914Arenas-Garcia, J., Gómes-Verdejo, V., Martínez-Ramon, M., Filgueiras-Vidal, A.R., Separate-variable adaptive combination of LMS adaptive filters for plant identification (2003) Proceedings of the IEEE XIII Workshop on Neural Network for Signal Processing, pp. 239-248. , ToulouseAttux, R.R.F., Loiola, M.B., Suyama, R., De Castro, L.N., Von Zuben, F.J., Romano, J.M.T., Blind search for optimal wiener equalizers using an artificial immune network model (2003) EURASIP Journal on Applied Signal Processing, 8, pp. 740-747Benveniste, A., Goursat, M., Blind equalizers (1984) IEEE Trans. on Communications, COM-32 (8), pp. 871-883De Castro, L.N., Timmis, J., (2002) Artificial Immune Systems: A New Computational Intelligence Approach, , Springer-VerlagDe Castro, L.N., Timmis, J., An artificial immune network for multimodal function optimization (2002) Proceedings of the IEEE Congress on Evolutionary Computation, 1, pp. 699-1674. , HawaiiDe França, F.O., Von Zuben, F.J., De Castro, L.N., An artificial immune network for multimodal function optimization on dynamic environments (2005) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), 1, pp. 289-296. , Washington D.C., JuneGaspar, A., Collard, P., From GAs to artificial immune systems: Improving adaptation in time dependent optimization (1999) Proceedings of the Congress on Evolutionary Computation (CEC), pp. 1859-1866. , Washington D.CHaykin, S., (1996) Adaptive Filter Theory. 3rd Ed., , Prentice HallHaykin, S., The blind deconvolution problem (1994) Blind Deconvolution, , Haykin, S. (ed.), Prentice-HallJohnson Jr., C.R., Anderson, B.O., Godard blind equalisation surface characteristics: White, zero-mean binary source case (1995) Int. Journal of Adaptive Control and Signal Processing, 9, pp. 301-324Suyama, R., Attux, R.R.F., Romano, J.M.T., Bellanger, M., Relations entre les critère du module constant et de Wiener (2003) Proc. of the GRETSI Symp. on Signal and Image Processing, , Sep., ParisWalker, J., Garrett, S., (2003) Dynamic Function Optimisation: Comparing the Performance of Clonal Selection and Evolutionary Strategies, pp. 273-284. , Lecture Notes in Computer Science 2787. Edinburg
An Artificial Immune Network For Multimodal Function Optimization On Dynamic Environments
No full textMultimodal optimization algorithms inspired by the immune system are generally characterized by a dynamic control of the population size and by diversity maintenance along the search. One of the most popular proposals is denoted opt-aiNet (artificial immune network for optimization) and is extended here to deal with time-varying fitness functions. Additional procedures are designed to improve the overall performance and the robustness of the immune-inspired approach, giving rise to a version for dynamic optimization, denoted dopt-aiNet. Firstly, challenging benchmark problems in static multimodal optimization are considered to validate the new proposal. No parameter adjustment is necessary to adapt the algorithm according to the peculiarities of each problem. In the sequence, dynamic environments are considered, and usual evaluation indices are adopted to assess the performance of dopt-aiNet and compare with alternative solution procedures available in the literature. Copyright 2005 ACM.289296Allen, D., (1987) Timing, Genetic Requirements and Functional Consequences of Somatic Hypermutation during B-cell Development, Imm. Rev., 96, pp. 5-22Angeline, P.J., Evolutionary optimization versus particle swarm optimizationPhilosophy and performance differences (1998) Proc. Evol. Prog. VII, pp. 601-610. , V. W. Porto, N. Saravanan, D. Waagen, and A. E. Eiben, Eds. Berlin, Germany: Springer-VerlagAngeline, P.J., Tracking extrema in dynamic environments (1997) Lecture Notes in Computer Science, 1213. , Proceedings of the 6th International Conference on Evolutionary Programming (Indianapolis,Indiana, USA, Apr, 13-16), P. J. Angeline, R. G. Reynolds, J. R. McDonnell, and R. C. Eberhart, Eds.,Springer VerlagBazaraa, M.S., Sherali, H.D., Shetty, C.M., (1993) Nonlinear Programming: Theory and Algorithms, 2nd Ed., , WileyDe Castro, L.N., Von Zuben, F.J., Learning and optimization using the clonal selection principle (2002) IEEE Transactions on Evolutionary Computation, Special Issue on Artificial Immune Systems, 6 (3), pp. 239-251De Castro, L.N., Timmis, J., Artificial Immune Systems: A New Computational Intelligence Approach, , Springer-VerlagDe Castro, L.N., Timmis, J., An artificial immune network for multimodal function optimization (2002) Proceedings of the IEEE Congress on Evolutionary Computation (CEC'02), 1, pp. 699-1674. , May, HawaiiGaspar, A., Collard, P., From GAs to artificial immune systems: Improving adaptation in time dependent optimization Proceedings of the Congress on Evolutionary Computation, pp. 1859-1866. , Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzal (Eds)Holland, P.W.H., Garcia-Femandez, J., Williams, N.A., Sidow, A., Gene duplications and origins of vertebrate development (1994) Development Supplement, pp. 125-133Kelsey, J., Timmis, J., Hone, A., Chasing chaos Proceedings of the Congress on Evolutionary Computation, pp. 413-419. , R. Sarker, R. Reynolds, H. Abbass, T. Kay-Chen, R. McKay, D. Essam, and T. Gedeon, editors, Canberra. Australia, December. IEEELeung, Y., Wang, Y., An orthogonal genetic algorithm with quantization for global numerical optimization (2001) IEEE Trans. Evol. Comput., 5 (1), pp. 41-53Ohno, S., (1970) Evolution by Gene Duplication, , Allen and Unwin, LondonPerelson, A.S., (1989) Immune Network Theory, Imm. Rev., 110, pp. 5-36Siarry, P., Berthiau, G., Durbin, F., Haussy, J., Enhanced simulated annealing for globally minimizing functions of many-continuous variables (1990) Neural Networks, 3, pp. 467-483Timmis, J., Edmonds, C., A comment on opt-AINet: An immune network algorithm for optimisation (2004) Lecture Notes in Computer Science, 3102, pp. 308-317. , D. Kalyanmoy et al, editor, Genetic and Evolutionary Computation, SpringerTimmis, J., Edmonds, C., Kelsey, J., Assessing the performance of two immune inspired algorithms and a hybrid genetic algorithm for function optimisation (2004) Proceedings of the Congress on Evolutionary Computation, 1, pp. 1044-1051Walker, J., Garrett, S., Dynamic function optimisation: Comparing the performance of clonal selection and evolutionary strategies Lecture Notes in Computer Science, 2787, pp. 273-284. , Timmis, J., Bentley, P. and Hart, E.(Eds)Yao, X., Liu, Y., Fast evolution strategies (1997) Evolutionary Programming VI, pp. 151-161. , P. J. Angeline, R. Reynolds, J. McDonnell, and R. Eberhart, Eds. Berlin, Germany: Springer-Verla
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