A Complex Neighborhood Based Particle Swarm Optimization

This paper proposes a new variant of the PSO algorithm named Complex Neighborhood Particle Swarm Optimizer (CNPSO) for solving global optimization problems. In the CNPSO, the neighborhood of the particles is organized through a complex network which is modified during the search process. This evolution of the topology seeks to improve the influence of the most successful particles and it is fine tuned for maintaining the scale-free characteristics of the network while the optimization is being performed. The use of a scale-free topology instead of the usual regular or global neighborhoods is intended to bring to the search procedure a better capability of exploring promising regions without a premature convergence, which would result in the procedure being easily trapped in a local optimum. The performance of the CNPSO is compared with the standard PSO on some wellknown and high-dimensional benchmark functions, ranging from multimodal to plateau-like problems. In all the cases theCNPSO outperformed the standard PSO. © 2009 IEEE.720727Barab́asi, L., (2003) Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life, , Plume Books, AprilBarab́asi, A.-L., Albert, R., Emergence of scaling in random networks (1999) Science, 286 (5439), pp. 509-512. , OctoberBarabási, A.-L., Jeong, H., Neda, Z., Ravasz, E., Schubert, A., Vicsek, T., Evolution of the social network of scientific collaborations (2002) Physica A, 311, p. 3Carlisle, A., Dozier, G., An off-the-shelf PSO (2001) Proceedings of the Workshop on Particle Swarm Optimization, pp. 1-6Clerc, M., Kennedy, J., The particle swarm - explosion, stability, and convergence in a multidimensional complex space (2002) Evolutionary Computation, IEEE Transactions on, 6 (1), pp. 58-73Dorogovtsev, S.N., Mendes, J.F.F., Evolution of networks (2001) ArXiv Condensed Matter e-prints, , JuneEberhart, R., Kennedy, J., A new optimizer using particle swarm theory (1995) Micro Machine and Human Science 1995. 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The Influence Of The Pool Of Candidates On The Performance Of Selection And Combination Techniques In Ensembles

In this paper, we propose the use of an immune-inspired approach called opt-aiNet to generate a diverse set of high-performance candidates to compose an ensemble of neural network classifiers. Being a population-based search algorithm, the opt-aiNet is capable of maintaining diversity and finding many high-performance solutions simultaneously, which are known to be desired features when synthesizing an ensemble. Concerning the selection and combination phases, the most relevant selection and combination techniques already proposed in the literature have been considered. The main contribution of this paper is the indication that there is no pair of selection/combination technique that can be considered the best one, because the performance of the obtained ensemble varies significantly with the current composition of the pool of candidates already produced by the generation phase. Notwithstanding, this variability in performance is not restricted to the choice of opt-aiNet as the generative device. As a consequence, to overcome the performance of the best individual classifier, every possible pairs of selection and combination techniques should be tried. Only with such an exhaustive search (notice that the main computational burden is usually related to the generation phase), the performance of the ensemble was invariably superior to the performance of the best individual classifier on four benchmark classification problems. ©2006 IEEE.51325139Hansen, L., Salamon, P., Neural network ensembles (1990) IEEE Trans. on Pattern Anal. and Machine Intelligence, 12, pp. 993-1005Hashem, S., Schmeiser, B., Yih, Y., Optimal linear combinations of neural, networks: An overview (1994) Proc. IEEE Int. Conf. on Neural Networks, , OrlandoBrown, G., Wyatt, J., Harris, R., Yao, X., Diversity Creation Methods: A Survey and Categorisation (2005) Journal of Information Fusion, 6 (1), pp. 5-20. , JanuaryLiu, Y., Negative correlation learning and evolutionary neural network ensembles, (1998), Ph.D. thesis, University College, The University of New South Wales, Australian Defense Force Academy, Canberra(1999) Artificial Immune Systems and their Applications, , D. Dasgupta ed, Springer-Verlagde Castro, L.N., Timmis, J., (2002) An Introduction to Artificial Immune Systems: A New Computational Intelligence Paradigm, , Springer-Verlagde Castro, L.N., Timmis, J., An artificial immune network for multimodal function optimization (2002) Proc. 2002 Conf. on Evolutionary Computation, 1, pp. 699-704. , 12-17 MayCastro, P.A.D., Coelho, G.P., Caetano, M., Von Zuben, F.J., Ensembles of Fuzzy Classification Systems: An Immune-Inspired Approach (2005) Lecture Notes in Computer Science, 3627, pp. 469-482. , C. Jacob et al. editors, Springer-VerlagG. P. Coelho, P. A. D. Castro, and F. J. Von Zuben, The Effective use of Diverse Rule Bases in Fuzzy Classification. In A. D. D. Neto et al. eds., VII Congresso Brasileiro de Redes Neurais, Brazil, 2005Zhou, Z.H., Wu, J., Tang, W., Ensembling Neural Networks: Many Could be Better Than All (2002) Artificial Intelligence, 137 (1-2), pp. 239-263Bezerra, G.B., Barra, T.V., de Castro, L.N., Von Zuben, F.J., Adaptive Radius Immune Algorithm for Data Clustering (2005) Lecture Notes in Computer Science, 3627, pp. 290-303. , C. Jacob et al. editors, Springer-VerlagTorres-Sospedra, J., Fernández-Redondo, M., Hernández-Espinosa, C., A Research on Combination. Methods for Ensembles of Multilayer Feedforward (2005) Proc. Int. Joint Conf. on Neural Networks, pp. 1125-1130L. N. de Castro, and F. J. Von Zuben, aiNet: An Artificial Immune Network for Data Analysis. In H. A. Abbass, R. A. Sarker, and C. S. Newton, editors, Data Mining: A Heuristic Approach, Idea Group Publishing, USA, Chapter XII, pp. 231-259, 2001Attux, 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 J. on Applied Signal Proc, 2003 (8), pp. 740-747Coello Coello, C.A., Cruz Cortés, N., Solving Multiobjective Optimization Problems Using an Artificial Immune System (2005) Genetic Programming and Evolvable Machines, 6 (2), pp. 163-190Ada, G.L., Nossal, G.J.V., The Clonal Selection Theory (1987) Scientific American, 257 (2), pp. 50-57N. K. Jerne, Towards a Network Theory of the Immune System. Ann. Immunol., (Inst. Pasteur) 125C, pp. 373-389, 1974Perrone, M.P., Cooper, L.N., When networks disagree: Ensemble method for neural networks (1993) Artificial Neural Networks for Speech and Vision, pp. 126-142. , R. J. Mamone, ed, Chapman & Hall, ppLiu, Y., Yao, S., Higuchi, T., Evolutionary Ensembles with Negative Correlation Learning (2000) IEEE Trans. on Evolutionary Computation, 4 (4), pp. 380-387. , NovemberBlake, C.L., Merz, C.J., (1998) UCI Repository of machine learning databases, , Irvine, CA: University of California, Department of Information and Computer ScienceInoue, H., Narihisa, H., Effective Online Pruning Method for Ensemble Self-Generating Neural Networks (2004) Proc. of IEEE Int. Midwest Symp. on Circuits and Systems, pp.III-85-III-88Lazarevic, A., Obradovic, Z., The Effective Pruning of Neural Network Ensembles (2001) Proc. IEEE/INNS Int. Conf. on Neural Neural Networks, pp. 796-801. , Washington, D.C, pp, Jul

Fault Detection Algorithm For Telephone Systems Based On The Danger Theory

This work is aimed at presenting a fault detection algorithm composed of multiple interconnected modules, and operating according to the paradigm supported by the danger theory in immunology. This algorithm attempts to achieve significant features that a fault detection system is supposed to have when monitoring a telephone profile system. These features would basically be adaptability due to the strong variation that operational conditions may exhibit over time, and the decrease in the number of false positives, which can be generated when any abnormal behavior is erroneously classified as being a fault. Simulated scenarios have been conceived to validate the proposal, and the obtained results are then analyzed. © Springer-Verlag Berlin Heidelberg 2005.3627418431Aickelin, U., Bentley, P., Cayzer, S., Kim, J., McLeod, J., Danger theory: The link between AIS and IDS? (2003) 2nd International Conference on AIS (ICARIS 2003), pp. 147-155Atamas, S.P., Les affinities electives (2005) Dossier Pour la Science, 46Ayara, M., Timmis, J., De Lemos, R., De Castro, L.N., Duncan, R., Negative selection: How to generate detectors (2002) 1st International Conference on AIS (ICARIS 2002), pp. 89-98Bersini, H., Self-assertion versus self-recognition: A tribute to Francisco Varela (2002) 1st International Conference on AIS (ICARIS 2002), pp. 107-112De Castro, L.N., Von Zuben, F.J., Learning and optimization using the clonal selection principle (2002) IEEE Transactions on Evolutionary Computation, 6 (3), pp. 239-251Faynberg, I., Lawrence, G., Lu, H.-L., (2000) Converged Networks and Services: Internetworking IP and the PSTN, , New York: John Wiley & SonsGonzález, F.A., Dasgupta, D., An immunogenetic technique to detect anomalies in network traffic (2002) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 1081-1088Hersent, O., Petit, J.P., (1999) IP Telephony: Packet-based Multimedia Communications Systems, , Addison-WesleyMatzinger, P., Tolerance danger and the extended family (1994) Annual Review of Immunology, 12, pp. 991-1045Sarafijanovic, S., Boudec, J., An artificial immune system for misbehavior detection in mobile ad-hoc networks with virtual thymus, clustering, danger signal, and memory detectors (2004) 3rd International Conference on AIS (ICARIS 2004), pp. 316-329Seeker, A., Freitas, A.A., Timmis, J., A danger theory inspired approach to web mining (2003) 2nd International Conference on AIS, pp. 156-167Schapire, R.E., Freund, Y., Bartlett, P., Lee, W.S., Boosting the margin: A new explanation for the effectiveness of voting methods (1998) The Annals of Statistics, 26 (5), pp. 1651-1686Venkatasubramanian, V., Rengaswamy, R., Yin, K., Kavuri, S.N., A review of process fault detection and diagnosis: Part I - Quantitative model-based methods (2003) Computer and Chemical Engineering, 27, pp. 293-31

Online Learning In Estimation Of Distribution Algorithms For Dynamic Environments

In this paper, we propose an estimation of distribution algorithm based on an inexpensive Gaussian mixture model with online learning, which will be employed in dynamic optimization. Here, the mixture model stores a vector of sufficient statistics of the best solutions, which is subsequently used to obtain the parameters of the Gaussian components. This approach is able to incorporate into the current mixture model potentially relevant information of the previous and current iterations. The online nature of the proposal is desirable in the context of dynamic optimization, where prompt reaction to new scenarios should be promoted. To analyze the performance of our proposal, a set of dynamic optimization problems in continuous domains was considered with distinct levels of complexity, and the obtained results were compared to the results produced by other existing algorithms in the dynamic optimization literature. © 2011 IEEE.6269Jin, Y., Branke, J., Evolutionary optimization in uncertain environments - A survey (2005) IEEE Transactions on Evolutionary Computation, 9 (3), pp. 303-317. , DOI 10.1109/TEVC.2005.846356Tinos, R., Yang, S., A self-organizing random immigrants genetic algorithm for dynamic optimization problems (2007) Genetic Programming and Evolvable Machines, 8 (3), pp. 255-286. , DOI 10.1007/s10710-007-9024-zYang, S., Yao, X., Population-based incremental learning with associative memory for dynamic environments (2008) Evolutionary Computation, IEEE Transactions on, 12 (5), pp. 542-561Mendes, R., Kennedy, J., Neves, J., The fully informed particle swarm: Simpler, maybe better (2004) Evolutionary Computation, IEEE Transactions on, 8 (3), pp. 204-210. , JuneLi, X., Branke, J., Blackwell, T., Particle swarm with speciation and adaptation in a dynamic environment (2006) GECCO 2006 - Genetic and Evolutionary Computation Conference, 1, pp. 51-58. , GECCO 2006 - Genetic and Evolutionary Computation ConferenceDe França, F., Von Zuben, F., De Castro, L., An artificial immune network for multimodal function optimization on dynamic environments (2005) Proceedings of the 2005 Conference on Genetic and Evolutionary Computation. ACM, p. 296De França, F., Zuben, F.V., A dynamic artificial immune algorithm applied to challenging benchmarking problems (2009) Proceedings of the Eleventh Conference on Congress on Evolutionary Computation, Ser. CEC'09, pp. 423-430. , Piscataway, NJ, USA: IEEE PressYang, S., Yao, X., Experimental study on population-based incremental learning algorithms for dynamic optimization problems (2005) Soft Computing, 9 (11), pp. 815-834. , DOI 10.1007/s00500-004-0422-3Liu, X., Wu, Y., Ye, J., An improved estimation of distribution algorithm in dynamic environments (2008) Fourth International Conference on Natural Computation. 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Series B (Methodological), 39 (1), pp. 1-38Nowlan, S., (1991) Soft Competitive Adaptation: Neural Network Learning Algorithms Based on Fitting Statistical Mixtures, , Ph. D. dissertation, Carnegie Mellon University, Pittsburgh, PA, USANeal, R., Hinton, G., A view of the EM algorithm that justifies incremental, sparse and other variants (1998) Learning in Graphical Models. Kluwer Academic Publishers, pp. 355-368Branke, J., Memory enhanced evolutionary algorithms for changing optimization problems (1999) Congress on Evolutionary Computation CEC99, 3, pp. 1875-1882Li, C., Yang, S., A generalized approach to construct benchmark problems for dynamic optimization (2008) Proc. of the 7th Int. Conf. on Simulated Evolution and LearningLi, C., Yang, S., Nguyen, T., Yu, E., Yao, X., Jin, Y., Beyer, H., Suganthan, P., Benchmark generator for CEC'2009 competition on dynamic optimization (2008) University of Leicester, Tech. Rep.Yuan, B., Orlowska, M., Sadiq, S., Extending a class of continuous estimation of distribution algorithms to dynamic problems (2008) Optimization Letters, 2 (3), pp. 433-443. , DOI 10.1007/s11590-007-0071-4Cobb, H., An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments (1990) Naval Research Laboratory, Tech. Rep

Training Multilayer Perceptrons With A Gaussian Artificial Immune System

In this paper we apply an immune-inspired approach to train Multilayer Perceptrons (MLPs) for classification problems. Our proposal, called Gaussian Artificial Immune System (GAIS), is an estimation of distribution algorithm that replaces the traditional mutation and cloning operators with a probabilistic model, more specifically a Gaussian network, representing the joint distribution of promising solutions. Subsequently, GAIS utilizes this probabilistic model for sampling new solutions. Thus, the algorithm takes into account the relationships among the variables of the problem, avoiding the disruption of already obtained high-quality partial solutions (building blocks). Besides the capability to identify and manipulate building blocks, the algorithm maintains diversity in the population, performs multimodal optimization and adjusts the size of the population automatically according to the problem. These attributes are generally absent from alternative algorithms, and all were shown to be useful attributes when optimizing the weights of MLPs, thus guiding to high-performance classifiers. GAIS was evaluated in six well-known classification problems and its performance compares favorably with that produced by contenders, such as opt-aiNet, IDEA and PSO. © 2011 IEEE.12501257Haykin, S., (1998) Neural Networks: A Comprehensive Foundation, , 2nd edition, Prentice Hall PTRYao, X., Evolving artificial neural networks (1999) Proceedings of the IEEE, 87 (9), pp. 1423-1447. , DOI 10.1109/5.784219Bao, J., Zhou, B., Yan, Y., A genetic-algorithm-based weight discretization paradigm for neural networks (2009) Proc. World Congress on Computer Science and Information Engineering, pp. 655-659Blum, C., Socha, K., Training feed-forward neural networks with ant colony optimization: An application to pattern classification (2005) Proc. 5th Intern. Conf. on Hybrid Intelligent Systems (HlS-2005), pp. 10-15Teixeira, L.A., Oliveira, F.T.G., Oliveira, A.L.I., Bastos Filho, C.J.A., Adjusting weights and architecture of neural networks through pso with time-varying parameters and early stopping (2008) Brazilian Symposium on Neural Networks, pp. 33-38Chen, Y., Zhang, Y., Abraham, A., Estimation of distribution algorithm for optimization of neural networks for intrusion detection system (2006) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4029, pp. 9-18. , Artificial Intelligence and Soft Computing - ICAISC 2006 - 8th International Conference, ProceedingsPasti, R., De Castro, L.N., Bio-inspired and gradient-based algorithms to train MLPs: The influence of diversity (2009) Information Sciences, 179 (10), pp. 1441-1453Timmis, J., Hone, A., Stibor, T., Clark, E., Theoretical advances in artificial immune systems (2008) Theoretical Computer Science, 403 (1), pp. 11-32De 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 S1089778X02060654Holland, J.H., (1992) Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, , MIT PressCastro, P.A.D., Von Zuben, F.J., BAIS: A bayesian artificial immune system for the effective handling of building blocks (2009) Information Sciences, 179 (10), pp. 1426-1440Pelikan, M., Goldberg, D.E., Lobo, F.G., A survey of optimization by building and using probabilistic models (2002) Computational Optimization and Applications, 21 (1), pp. 5-20. , DOI 10.1023/A:1013500812258Castro, P.A.D., Von Zuben, F.J., Learning ensembles of neural networks by means of a bayesian artificial immune system (2011) IEEE Transactions on Neural Networks, 22 (2), pp. 304-316Castro, P.A.D., Von Zuben, F.J., Feature subset selection by means of a Bayesian artificial immune system (2008) Proc. 8th Int. Conf. on Hybrid Intelligent Systems, pp. 561-566Castro, P.A.D., Von Zuben, F.J., MOBAIS: A Bayesian artificial immune system for multi-objective optimization (2008) Lecture Notes in Computer Science, 5132, pp. 48-59. , SpringerCastro, P.A.D., Von Zuben, F.J., Multi-objective Bayesian artificial immune system: Empirical evaluation and comparative analyses (2009) Journal of Mathematical Modelling and Algorithms, 1, pp. 151-173Castro, P.A.D., Von Zuben, F.J., Multi-objective feature selection using a Bayesian artificial immune system (2010) Journal of Intelligent Computing and Cybernetics, 3 (2), pp. 235-256Castro, P.A.D., Von Zuben, F.J., GAIS: A gaussian artificial immune system for continuous optimization (2010) Proc. 9th Int. Conf. on Artificial Immune SystemsCastro, P.A.D., Von Zuben, F.J., A gaussian artificial immune system for multi-objective optimization in continuous domains (2010) Proc. 10th International Conference on Hybrid Intelligent Systems, pp. 159-164. , Atlanta, USAGeiger, D., Heckerman, D., Learning gaussian networks (1994) Technical Report MSR-TR-94-10, , Microsoft ResearchShachter, R.D., Kenley, C.R., Gaussian influence diagrams (1989) Management Science, 35 (5), pp. 527-550Lu, Q., Yao, X., Clustering and learning Gaussian distribution for continuous optimization (2005) IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 35 (2), pp. 195-204. , DOI 10.1109/TSMCC.2004.841914Ripley, B.D., (1987) Stochastic Simulation, , John Wiley & SonsFrank, A., Asuncion, A., (2010) UCI Machine Learning Repository, , http://archive.ics.uci.edu/ml, University of California, Irvine, School of Information and Computer SciencesDe Castro, L.N., Timmis, J.I., An artificial immune network for multimodal function optimization (2002) Proc. IEEE Congress of Evolutionary Computation, 1, pp. 699-674Bosman, P., Thierens, D., Expanding from discrete to continuous estimation of distribution algorithms: The IDEA (2000) Parallel Problem Solving From Nature, 6, pp. 767-776Hartigan, J.A., (1975) Clustering Algorithms, , Wiley New YorkBroom, B., Subramanian, D., Computational methods for learning bayesian networks from high-throughput biological data (2006) Bayesian Inference for Gene Expression and Proteomics, , Cambridge Universit

Immune Learning Classifier Networks: Evolving Nodes And Connections

The design of an autonomous navigation system with multiple tasks to be accomplished in unknown environments represents a complex undertaking. With the simultaneous purposes of capturing targets and avoiding obstacles, the challenge may become still more intricate if the configuration of obstacles and targets creates local minima, like concave shapes and mazes between the robot and the target. Pure reactive navigation systems are not able to deal properly with such hampering scenarios, requiring additional cognitive apparatus. Concepts from immune network theory are then employed to convert an earlier reactive robot controller, based on learning classifier systems, into a connectionist device. Starting from no a priori knowledge, both the classifiers and their connections are evolved during the robot navigation. Some experiments with and without local minima are carried out and the proposed evolutionary network of classifiers was shown to produce connectionist navigation systems capable of successfully overcoming local minima. © 2006 IEEE.22302237Deb, K., (2001) Multi-Objective Optimization Using Evolutionary Algorithms, , Chichester, UK: WileyRam, A., Arkin, R.C., Moorman, K., Clark, R.J., Case-based reactive navigation: A method for on-line selection and adaptation of reactive robotic control parameters (1997) IEEE Trans, on Systems, Man, and Cybernetics, Part B, 27 (3), pp. 376-394Krishna, K.M., Kalra, P.K., Solving the local minima problem for a mobile robot by classification, of spatio-temporal sensory sequences (2000) Journal of Robotic Systems, 17, pp. 549-564. , OctFodor, J.A., Pylyshyn, Z.W., Connectionism and cognitive architecture: A critical analysis (1988) Cognition, 28, pp. 3-72P. Smolensky, On the proper treatment of connectionism, University of Colorado, Dept. of Computer Science, Boulder, CO, Tech. Rep. CU-CS-377-87, 1987Farmer, J., A rosetta stone for connectionism (1990) Physica D, 42 (1-3), pp. 153-187Bates, E., Elman, J., Connectionism. and the study of change (2002) Brain development and cognition: A reader, , 2nd ed, M. Johnson, Ed. Oxford: Blackwell PublishersCazangi, R.R., Von Zuben, F.J., Figueiredo, M.F., A. classifier system in real applications for robot navigation (2003) Proc. of the 2003 CEC, 1, pp. 574-580. , Canberra, Australia: IEEE PressCazangi, R.R., Von Zuben, F.J., Figueiredo, M.F., Autonomous navigation, system applied to collective robotics with ant-inspired communication (2005) Proc. of the 2005 GECCO, 1, pp. 121-128. , Washington DC, USA: ACM Press_, Stigmergic autonomous navigation in collective robotics, in Stigmergic Optimization, A. Abraham, C. Grosan, and V. Ramos, Eds. Springer-Verlag, 2006Holland, J., Escaping brittleness: The possibilities of general purpose learning algorithms applied to parallel rule-based systems (1986) Machine Intelligence II, , R. Michalsky, J. Carbonell, and T. Mitchell, Eds. Morgan KaufmannHershberg, U., Efroni, S., The immune system, and other cognitive systems (2001) Complexity, 6 (5), pp. 14-21de Castro, L.N., Immune cognition, micro-evolution, and a personal account on immune engineering (2003) S.E.E.D. Journal, 3 (3), pp. 134-155Jerne, N.K., Towards a network theory of the immune system (1974) Ann. Immunol, 125 C, pp. 373-389Farmer, J., Packard, N., Perelson, A., The immune system, adaptation, and machine learning (1986) Physica, 22 D, pp. 187-204de Castro, L.N., Timmis, J., (2002) Artificial Immune Systems: A New Computational Intelligence Paradigm, , Springer-VerlagLumelsky, V., A comparative study on the path length performance of maze-searching and robot motion planning algorithms (1991) IEEE Trans. on Robotics and Automation, 7 (1), pp. 57-66Kube, C., Parker, C., Wang, T., Zhang, H., Biologically inspired collective robotics (2004) Recent Developments in. Biologically Inspired Computing, , L. de Castto and F. Von Zuben, Eds. Idea GroupNolfi, S., Floriano, D., (2000) Evolutionary Robotics, , MIT PressParisi, D., Calabretta, R., (2001) Evolutionary connectionism. and mind/brain modularity, , Institute of Psychology, National Research CouncilRome, Italy, Tech. Rep. NSAL 01-01Kim, K.-J., Yoo, J.-O., Cho, S.-B., Robust inference of bayesian networks using speciated evolution and ensemble (2005) ISMIS, pp. 92-101Nolfi, S., Floreano, D., Miglino, O., Mondada, F., How to evolve autonomous robots: Different approaches in evolutionary robotics (1994) Proc. of the 4th International Workshop on the Synthesis and Simulation of Living Systems ArtificialLifeIV, pp. 190-197Vasilyev, A., Autonomous agent control using connectionist XCS classifier system (2002) Transport and Telecommunication, 3 (3), pp. 56-63P. A, Vargas, L. N. de Castro, R. Michelan, and F. J. Von. Zuben, An immune learning classifier network for autonomous navigation, in Proc. of the Second ICARIS, 2003, pp. 69-8

Feature Subset Selection By Means Of A Bayesian Artificial Immune System

This paper proposes the application of a novel bio-inspired algorithm as a search engine to the feature subset selection problem. We may interpret our algorithm as an Estimation of Distribution Algorithm that adopts an Artificial Immune System to implement the search process in the space of all features and a Bayesian network to implement the probabilistic model of the promising solutions. The characteristics of the proposed algorithm are the capability of effectively identifying and manipulating building blocks, maintenance of diversity in the population, and automatic control of the population size. These properties allow the algorithm to perform a multimodal search, known to be of great relevance in feature selection problems. Experiments on five datasets were carried out in order to evaluate the proposed methodology in classification problems and its performance compares favorably to that produced by contenders. © 2008 IEEE.561566Almuallim, H., Dietterich, T.G., Learning with many irrelevant features (1991) Proc. of the 9th National. Conf. on Artificial Intelligence, 2, pp. 547-552Bala, J., DeJong, K., Huang, J., Vafaie, H., Wechsler, H., Using learning to facilitate the evolution of features for recognizing visual concepts (1996) Evolutionary Computation, 4 (3), pp. 297-311Blake, C.L., Merz, C.J., (1998) UCI repository of machine learning databases, , http://www.ics.uci.edu/~mlearn/MLRepository.html, Irvine, CABrotherton, T., Simpson, P., Dynamic feature set training of neural nets for classification (1995) Evolutionary Programming IV, pp. 83-94Cantú-Paz, E., Feature subset selection by estimation of distribution algorithms (2002) Proc. of the Genetic and Evolutionary Computation Conference, pp. 303-310Castro, P.A.D., Santoro, D.M., Camargo, H.A., Nicoletti, M.C., Improving a Pittsburgh learnt fuzzy rule base using feature subset selection (2004) 4th Int. Conf. on Hybrid Intelligent Systems, pp. 180-185Castro, P.A.D., Von Zuben, F.J., Bayesian learning of neural networks by means of artificial immune systems (2006) Proc. of the 5th IJCNN, pp. 9885-9892Castro, P.A.D., Von Zuben, F.J., MOBAIS: A Bayesian Artificial Immune System for Multi-objective Optimization (2008) Proc. of the 7th Int. Conf. on Artificial Immune Systems, pp. 48-59Castro, P.A.D., Von Zuben, F.J., BAIS: A Bayesian Artificial Immune System for Effective Handling of Building Blocks (2008) Information Sciences - Special Issue on Artificial Immune Systems, , to appearCooper, G., Herskovits, E., A Bayesian method for the induction of probabilistic networks from data (1992) Machine Learning, 9, pp. 309-347de Castro, L.N., Timmis, J., (2002) Artificial Immune Systems: A New Computational Intelligence Approach, , Springer Verlag, LondonGuyon, I., Elisseeff, A., An introduction to variable and feature selection (2003) J. Mach. Learn. Res, 3, pp. 1157-1182Henrion, M., Propagating uncertainty in Bayesian networks by probabilistic logic sampling (1988) Uncertainty in Artificial Intelligence, 2, pp. 149-163Holland, J.H., (1992) Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, , The MIT PressInza, I., naga, P.L., Etxeberria, R., Sierra, B., Feature subset selection by Bayesian network-based optimization (2000) Artificial Intelligence, 123, pp. 157-184Kira, K., Rendell, L.A., A practical approach to feature selection (1992) Proc. of the 9th Int. Workshop on Machine Learning, pp. 249-256Kohavi, R., John, G.H., Wrappers for feature subset selection (1997) Artificial Intelligence, 97 (1-2), pp. 273-324Kononenko, I., Estimating attributes: Analysis and extensions of RELIEF (1994) European Conference on Machine Learning, pp. 171-182Punch, W.F., Goodman, E.D., Pei, M., Chia-Shun, L., Hovland, P., Enbody, R., Further research on feature selection and classification using genetic algorithms (1993) Proc. of the 5th Int. Conf. on Genetic Algorithms, pp. 557-564Saeys, Y., Inza, I., Larrafiaga, P., A review of feature selection techniques in bioinformatics (2007) Bioinformatics, 23 (19), pp. 2507-2517Siedlecki, W., Sklansky, J., A note on genetic algorithms for large- scale feature selection (1989) Pattern Recogn. Lett, 10 (5), pp. 335-347Vafaie, H., DeJong, K., Robust feature selection algorithms (1993) Proc. 5th Intl. Conf. on Tools with Artificial Intelligence, pp. 356-36

Multiple Criteria Optimization Based On Unsupervised Learning And Fuzzy Inference Applied To The Vehicle Routing Problem

This paper presents a neuro-fuzzy system based on competitive learning to solve multiple criteria optimization problems. The proposed method promotes the simultaneous self-organization of several networks, employing unsupervised learning guided by a fuzzy rule base. The algorithm implements a policy of penalties and rewards, a strategy of neuron inhibition, insertion and pruning, and also takes into account certain statistical characteristics of the input space. A fuzzy inference system is designed to implement the decision making process under a multiobjective scenario, leading to an adaptive process of constraint relaxation. The effectiveness of the proposed method is attested by means of a series of computational simulations performed on standard data. In our simulations, we address two variants of the vehicle routing problem: the capacitated vehicle routing problem (CVRP) and the multiple traveling salesman problem (MTSP). There are a few works treating the vehicle routing problem by means of competitive learning. These approaches are briefly reviewed in this paper. We also present some improvements in the results of an implementation of tabu search by providing the solutions obtained by the neuro-fuzzy system as initial condition, showing that the proposed method can effectively produce satisfactory results when used in association with more dedicated approaches.1302/04/15143154Angéniol, B., Vaubois, C., Le Texier, J.Y., Self-organizing feature maps and the travelling salesman problem (1998) Neural Networks, 1, pp. 289-293Caprara, A., Fischetti, M., Branch-and-cut algorithms (1997) Annotated Bibliographies in Combinatorial Optimization, pp. 45-64. , M. Dell'Amico, F. Maffioli AND S. Martello, eds, WileyChristofides, N., Eilon, S., An algorithm for the vehicle dispatching problem (1969) Operational Research Quartely, 20, pp. 309-318Dantzig, G.B., Ramser, R.H., The truck dispatching problem (1959) Management Science, 6, pp. 80-91De Castro, L.N., Timmis, J., (2002) Artificial Immune Systems: A New Computational Intelligence Approach, , Springer-VerlagDorigo, M., Di Caro, G., Ant algorithms for discrete optimization (1999) Artificial Life, 2 (5), pp. 137-172Fogel, D.B., (1995) Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, , IEEE PressGarey, M.R., Johnson, D., (1979) Computers and Intractability: A Guide to the Theory of NP-completeness, , FreemanGhaziri, H., Solving routing problems by a self-organizing map (1991) Artificial Neural Networks, pp. 829-834. , T. Kohonen, K. Makisara, O. Simula and J. Kangas, edsGlover, F., Laguna, M., (1997) Tabu Search, , Kluwer Academic PublishersGlover, F., Kochenberger, G.A., (2002) Handbook of Metaheuristics, , Kluwer Academic PublishersGomes, L.C.T., Von Zuben, F.J., A heuristic method based on unsupervised learning and fuzzy inference for the vehicle routing problem (2002) Proc. of VII Brazilian Symposium on Neural Networks, pp. 130-135Gong, D., Gen, M., Yamazaki, G., Xu, W., Neural network approach for general assignment problem (1995) Proc. of International Conference on Neural Networks, 4, pp. 1861-1866Kennedy, J., Eberhart, R.C., Shi, Y., (2001) Swarm Intelligence, , Morgan Kaufmann PublishersKirkpatrick, S., Gelatt, C.D., Vecchi, M.P., Optimization by simulated annealing (1983) Science, 4598 (220), pp. 671-680Kohonen, T., (1997) Self-organizing Maps, (2nd Ed.), , Springer VerlagMatsuyama, Y., Harmonic competition: A self-organizing multiple criteria optimization (1996) IEEE Transactions on Neural Networks, 7 (3), pp. 652-668Modares, A., Somhom, S., Enkawa, T., A self-organizing neural network approach for multiple traveling salesman and vehicle routing problems (1999) Int. Transactions in Operational Research, 6, pp. 591-606Nemhauser, G.L., Wolsey, L.A., (1988) Integer and Combinatorial Optimization, , John Wiley and SonsSmith, K.A., Neural networks for combinatorial optimization: A review of more than a decade of research (1999) INFORMS Journal on Computing, 11 (1), pp. 15-34Toth, P., Vigo, D., (2002) The Vehicle Routing Problem, , Society for Industrial and Applied Mathematics (SIAM)Vakhutinsky, A.I., Golden, B.L., Solving vehicle routing problems using elastic nets (1994) Proc. of IEEE Int. Conf. on Neural Networks, 7, pp. 4535-4540Zadeh, L.A., Fuzzy sets (1965) Inform. Control, 8, pp. 338-35

Multi-objective Bayesian Artificial Immune System: Empirical Evaluation And Comparative Analyses

Recently, we have proposed a Multi-Objective Bayesian Artificial Immune System (MOBAIS) to deal effectively with building blocks (high-quality partial solutions coded in the solution vector) in combinatorial multi-objective problems. By replacing the mutation and cloning operators with a probabilistic model, more specifically a Bayesian network representing the joint distribution of promising solutions, MOBAIS takes into account the relationships among the variables of the problem, avoiding the disruption of already obtained high-quality partial solutions. The preliminary results have indicated that our proposal is able to properly build the Pareto front. Motivated by this scenario, this paper better formalizes the proposal and investigates its usefulness on more challenging problems. In addition, an important enhancement regarding the Bayesian network learning was incorporated into the algorithm in order to speed up its execution. To conclude, we compare MOBAIS with state-of-the-art algorithms taking into account quantitative aspects of the Pareto front found by the algorithms. MOBAIS outperforms the contenders in terms of the quality of the obtained solutions and requires an amount of computational resource inferior or compatible with the contenders. © 2009 Springer Science+Business Media B.V.82151173Ada, G.L., Nossal, G.J.V., The clonal selection theory (1987) Sci. Am., 257 (2), pp. 50-57Baluja, S., (1994) Population-based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning, , Technical Report, Carnegie Mellon University, PittsburghBaluja, S., Davies, S., Using optimal dependency-trees for combinational optimization (1997) Proc. of the 14th Int. Conf. on Machine Learning, pp. 30-38. , San FranciscoDe Bonet, J.S., Isbell, C.L., MIMIC: Finding optima by estimating probability densities (1997) Adv. Neural Inf. Process. Syst., 9, p. 424Castro, P.A.D., Von Zuben, F.J., Bayesian learning of neural networks by means of artificial immune systems (2006) Proc. of the 5th Int. Joint Conf. on Neural Networks, pp. 9885-9892Castro, P.A.D., Von Zuben, F.J., BAIS: A Bayesian artificial immune system for the effective handling of building blocks (2009) Inf. Sci., , in pressCastro, P.A.D., Von Zuben, F.J., Feature subset selection by means of a Bayesian artificial immune system (2008) Proc. of the 8th Int. Conf. on Hybrid Intelligent Systems, pp. 561-56Castro, P.A.D., Von Zuben, F.J., MOBAIS: A Bayesian artificial immune system for multi-objective optimization (2008) Proc. of the 7th Int. Conf. on Artificial Immune Systems, pp. 48-59Chen, J., Mahfouf, M., Bersini, H., Carneiro, J., A population adaptive based immune algorithm for solving multi-objective optimization problems (2006) Lecture Notes in Computer Sciences - Artificial Immune Systems, Vol. 4163, pp. 280-293. , Springer New YorkChickering, D.M., Learning Bayesian networks is NP-complete (1996) Learning from Data: Artificial Intelligence and Statistics v, pp. 121-130. , Springer New YorkCoelho, G.P., Von Zuben, F.J., Bersini, H., Carneiro, J., Omni-aiNet: An immune-inspired approach for omni optimization (2006) Lecture Notes in Computer Sciences-Artificial Immune Systems, Vol. 4163, pp. 294-308. , Springer New YorkCoello Coello, C., An approach to solve multiobjective optimization problems based on an artificial immune system (2002) Proc. of the 1st Int. Conf. on Artificial Immune System, pp. 212-221Coello Coello, C., Cortés, N.C., Solving multiobjective optimization problems using an artificial immune system (2005) Genet. Program. Evolv. Mach., 6 (2), pp. 163-190Cooper, G., Herskovits, E., A Bayesian method for the induction of probabilistic networks from data (1992) Mach. Learn., 9, pp. 309-347Dasgupta, D., Advances in artificial immune systems (2006) IEEE Computational Intelligence Magazine, 1 (4), pp. 40-43. , DOI 10.1109/CI-M.2006.248056De Castro, L.N., Timmis, J., (2002) Artificial Immune Systems: A New Computational Intelligence Approach, , Springer New YorkDe Castro, L.N., Von Zuben, F.J., Learning and optimization using the clonal selection principle (2002) IEEE Trans. Evol. Comput., 6 (3), pp. 239-251De Castro, L.N., Timmis, J., An artificial immune network for multimodal optimisation (2002) Proc. of the IEEE World Congress on Evolutionary Computation, pp. 669-674Deb, K., Multi-objective genetic algorithms: Problem difficulties and construction of test problems (1999) Evol. Comput., 7, pp. 205-230Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., A fast and elitist multiobjective genetic algorithm: NSGA-II (2002) IEEE Transactions on Evolutionary Computation, 6 (2), pp. 182-197. , DOI 10.1109/4235.996017, PII S1089778X02041012Deb, K., Tiwari, S., Omni-optimizer: A procedure for single and multi-objective optimization (2005) Proc. of the of EMO, pp. 47-61Freschi, F., Repetto, M., VIS: An artificial immune network for multi-objective optimization (2006) Engineering Optimization, 38 (8), pp. 975-996. , DOI 10.1080/03052150600880706, PII U33283783QQ71P54Goldberg, D.E., Deb, K., Kargupta, H., Harik, G., Rapid accurate optimization of difficult problems using fast messy genetic algorithms (1993) Proc. of the Fifth Int. Conf. on Genetic Algorithms, pp. 56-64. , Morgan Kaufmann San FranciscoGoldberg, D.E., Korb, G., Deb, K., Messy genetic algorithms: Motivation, analysis, and first results (1989) Complex Syst., 3, pp. 493-530Goldberg, D.E., (1989) Genetic Algorithms in Search, Optimization, and Machine Learning, , Addison-Wesley ReadingHenrion, M., Propagating uncertainty in Bayesian networks by probabilistic logic sampling (1998) Uncertainty Artif. Intell., 2, pp. 149-163Holland, J.H., (1992) Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, , MIT CambridgeJerne, N.K., Towards a network theory of the immune system (1974) Ann. Immunol. (Inst. Pasteur), 125 C, pp. 373-389Khan, N., Goldberg, D.E., Pelikan, M., (2002) Multi-objective Bayesian Optimization Algorithm, , Technical report, University of Illinois, Illigal Report 2002009Luh, G.-C., Chueh, C.-H., Liu, W.-M., MOIA: Multi-objective immune algorithm (2003) Eng. Optim., 35 (2), pp. 143-164Mühlenbein, H., From recombination of genes to the estimation of distributions I. Binary parameters (1996) Proc. of the 4th Int. Conf. on Parallel Problem Solving from Nature, pp. 178-187Mühlenbein, H., Mahnig, T., FDA-a scalable evolutionary algorithm for the optimization of additively decomposed functions (1999) Evol. Comput., 7, pp. 353-376Osyczka, A., Gero, J.S., Multicriteria optimization for engineering design (1985) Design Optimization, pp. 193-227. , Academic LondonPelikan, M., Goldberg, D., Lobo, F., (1999) A Survey of Optimization by Building and Using Probabilistic Models, , Technical report, University of Illinois, ILLIGAL Report n 99018Pelikan, M., Mühlenbein, H., Roy, R., Furuhashi, T., Chawdhry, P.K., The bivariate marginal distribution algorithm (1999) Advances in Soft Computing-Engineering Design and Manufacturing, pp. 521-535. , Springer LondonPelikan, M., BOA: The Bayesian optimization algorithm (1999) Proc. of the Genetic and Evol. Comput. Conference, 1, pp. 525-532Pelikan, M., Goldberg, D., Lobo, F., A survey of optimization by building and using probabilistic models (2002) Comput. Optim. Appl., 21 (1), pp. 5-20Pelikan, M., Sastry, K., Cantú-Paz, E., (2005) Scalable Optimization Via Probabilistic Modeling: From Algorithms to Applications, , Springer New YorkPeña, J.M., Lozano, J.A., Larrañaga, P., Globally multimodal problem optimization via an estimation of distribution algorithm based on unsupervised learning of Bayesian networks (2005) Evol. Comput., 13, pp. 43-66Schott, J.R., (1995) Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization, , Master's thesis, Dept. of Aeronautics and Astronautics, Massachusetts Institute of TechnologyVan Veldhuizen, D.A., (1999) Multiobjective Evolutionary Algorithms: Classifications, Analysis, and New Innovations, , Ph.D. thesis, Graduate School of Engineering of the Air Force Inst. of Tech., Wright-Patterson AFBYoo, J., Hajela, P., Immune network simulations in multicriterion design (1999) Struct. Optim., 18, pp. 85-94Zitzler, E., Deb, K., Thiele, L., Comparison of multiobjective evolutionary algorithms: Empirical results (2000) Evol. Comput., 8 (2), pp. 173-195Zitzler, E., Thiele, L., Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach (1999) IEEE Trans. Evol. Comput., 3 (4), pp. 257-27

Bayesian Learning Of Neural Networks By Means Of Artificial Immune Systems

Once the design of Artificial Neural Networks (ANN) may require the optimization of numerical and structural parameters, bio-inspired algorithms have been successfully applied to accomplish this task, since they are population-based search strategies capable of dealing successfully with complex and large search spaces, avoiding local minima. In tills paper, we propose the use of an Artificial Immune System for learning feedforward ANN's topologies. Besides the number of neurons in the hidden layer, the algorithm also optimizes the type of activation function for each node. The use of a Bayesian framework to infer the weights and weight decay terms as well as to perform model selection allows us to find neural models with high generalization capability and low complexity, once the Occam's razor principle is incorporated into the framework. We demonstrate the applicability of the proposal on seven classification problems and promising results were obtained. © 2006 IEEE.48314838Ada, G.L., Nossal, G.J.V., The Clonal Selection Theory (1987) Scientific American, 257 (2), pp. 50-57Angeline, P.J., Sauders, G.M., Pollack, J.B., An evolutionary algorithm that constructs recurrent neural networks (1994) IEEE Trans. Neural Networks, 5, p. 5465Battiti, R., First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method (1992) Neural Computation, 4 (2), pp. 141-166Blake, C.L., Merz, C.J., (1998) UCI Repository of machine learning databases, , http://www.ics.uci.edu/~mlearn/MLRepository.html, Irvine, CA: University of California, Department of Information and Computer ScienceBishop, C.M., (1995) Neural Networks for Pattern Recognition, , Clarendon PressBuntine, W.L., Weigend, A.S., Bayesian backpropagation (1991) Complex Systems, 5, p. 603643Blum, C., Socha, K., Training feed-forward neural networks with ant colony optimization: An application to pattern classification (2005) Proc. 5th Intern. Conf. on Hybrid Intelligent Svstems(HIS-2005), pp. 10-15Castro, P.A.D., Coelho, G.P., Caetano, M.F., Von Zuben, F.J., Designing Ensembles of Fuzzy Classification Systems: An Immune-Inspired Approach (2005) Lectures Notes in Computer Science, 3627, pp. 469-482. , Springer-VerlagCastro, P.A.D., Von Zuben, F.J., An Immune-Inspired Approach to Bayesian Networks (2005) Proc. 5th Intern. Conf. on Hybrid Intelligent Systems (HIS-2005), pp. 23-28. , N. Nedjah et al, edsCoelho, G.P., Castro, P.A.D., Von Zuben, F.J., The Effective Use of Diverse Rule Bases in Fuzzy Classification (2005) VII Brazilian Congress on Neural NetworksDasgupta, D., (1999) Artificial Immune Systems and Their Applications, , Springer-Verlagde Castro, L.N., Timmis, J., (2002) An Introduction to Artificial Immune Systems: A New Computational Intelligence Paradigm, , Springer-Verlagde Castro, L.N., Timmis, J.I., An Artificial Immune Network for Multimodal Function Optimization (2002) Proceedings of IEEE Congress of Evolutionary Computation, 1, pp. 699-674de Castro, L.N., Von Zuben, F.J., An Immunological Approach to Initialize Feedforward Neural Network Weights (2001) Proc. Intern. Conf. on Artificial Neural Networks and Genetic Algorithms, pp. 126-129Gomes, L.C.T., de Sousa, J.S., Bezerra, G.B., de Castro, L.N., Von Zuben, F.J., Copt-aiNet and the Gene Ordering Problem (2003) Revista Tecnologia da Informação, 3 (2), pp. 27-33Haykin, S., (1998) Neural Networks: A Comprehensive Foundation, , 2nd edition, Prentice Hall PTRIyoda, E.M., Von Zuben, F.J., Hybrid Neural Networks: An Evolutionary Approach With Local Search (2002) Integrated Computer-Aided Engineering, 9 (1), pp. 57-72Jerne, N.K., Towards a Network Theory of the Immune System (1974) Ann. Immunol. (Inst. Pasteur), 125 C, pp. 373-389Jones, A.J., Genetic algorithms and their applications to the design of neural networks (1993) Neural Computing & Appl, 1, pp. 32-45Kwok, T.Y., Yeung, D.Y., Bayesian regularization in constructive neural networks (1996) Proc. Intern. Conf. on Artificial Neural Networks, p. 557562Kwok, T.Y., Yeung, D.Y., Constructive algorithms for structure learning in feedforward neural networks for regression problems (1997) IEEE Trans. on Neural Networks, 8 (3), pp. 630-645Liu, Y., Yao, X., Evolutionary design of artificial neural networks with different node transfer functions (1996) Proc. 3rd IEEE Intern. Conf. on Evolutionary Computation (CEC, pp. 670-675. , 96, ppMatteucci, M., Spadoni, D., Evolutionary Learning of Rich Neural Networks in the Bayesian Model Selection Framework (2004) Intern. Journal of Applied Mathematics and Computer Science, 14 (3), pp. 423-440MacKay, D.J.C., A Practical Bayesian Framework for Backpropagation Networks (1992) Neural Computation, 4 (3), pp. 448-472MacKay, D.J.C., Bayesian Interpolation (1992) Neural Computation, 4 (3), pp. 415-447MacKay, D.J.C., The Evidence Framework Applied to Classification Networks (1992) Neural Computation, 4 (5), pp. 698-714Moller, M.F., A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning (1993) Neural Networks, 6 (4), pp. 525-533Neal, R.M., (1996) Bayesian Learning for Neural Networks, , Springer-VerlagRussell, R., Pruning algorithm A survey (1993) IEEE Trans. on Neural Networks, 4 (5), pp. 740-747Thodberg, H.H., A review of Bayesian neural networks with an application to near infrared spectroscopy (1996) IEEE Trans. Neural Networks, 7, p. 5672Vertosick, F.T., Kelly, R.H., The Immune System as a Neural Network: A Multi-epitope Approach (1991) Journal of Theoretical Biology, 150, pp. 225-237Yang, J., Parekh, R., Honavar, V., DistAL: An interpattern distancebased constructive learning algorithm (1999) Intelligent Data Analysis, 3, pp. 55-73Yao, X., Evolving Artificial Neural Networks (1999) Proceedings of the IEEE, 87, pp. 1423-1447Zhang, B.T., A Bayesian Evolutionary Approach to the Design and Learning of Heterogeneous Neural Trees (2002) Integrated Computer-Aided Engineering, 9 (1), pp. 73-8