Artificial Immune System for Solving Global Optimization Problems

In this paper, we present a novel model of an artificial immune system (AIS), based on the process that suffers the T-Cell. The proposed model is used for global optimization problems. The model operates on four populations: Virgins, Effectors (CD4 and CD8) and Memory. Each of them has a different role, representation and procedures. We validate our proposed approach with a set of test functions taken from the specialized literature, we also compare our results with the results obtained by different bio-inspired approaches and we statistically analyze the results gotten by our approach.Fil: Aragon, Victoria Soledad. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo En Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Luis; ArgentinaFil: Esquivel, Susana C.. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; ArgentinaFil: Coello Coello, Carlos A.. CINVESTAV-IPN; Méxic

Immunity-based evolutionary algorithm for optimal global container repositioning in liner shipping

Global container repositioning in liner shipping has always been a challenging problem in container transportation as the global market in maritime logistics is complex and competitive. Supply and demand are dynamic under the ever changing trade imbalance. A useful computation optimization tool to assist shipping liners on decision making and planning to reposition large quantities of empty containers from surplus countries to deficit regions in a cost effective manner is crucial. A novel immunity-based evolutionary algorithm known as immunity-based evolutionary algorithm (IMEA) is developed to solve the multi-objective container repositioning problems in this research. The algorithm adopts the clonal selection and immune suppression theories to attain the Pareto optimal front. The proposed algorithm was verified with benchmarking functions and compared with four optimization algorithms to assess its diversity and spread. The developed algorithm provides a useful means to solve the problem and assist shipping liners in the global container transportation operations in an optimized and cost effective manner. © 2010 The Author(s).published_or_final_versionSpringer Open Choice, 21 Feb 201

When Evolutionary Computing Meets Astro- and Geoinformatics

International audienceKnowledge discovery from data typically includes solving some type of an optimization problem that can be efficiently addressed using algorithms belonging to the class of evolutionary and bio-inspired computation. In this chapter, we give an overview of the various kinds of evolutionary algorithms, such as genetic algorithms, evolutionary strategy, evolutionary and genetic programming, differential evolution, and coevolutionary algorithms, as well as several other bio-inspired approaches, like swarm intelligence and artificial immune systems. After elaborating on the methodology, we provide numerous examples of applications in astronomy and geoscience and show how these algorithms can be applied within a distributed environment, by making use of parallel computing, which is essential when dealing with Big Data

Representation and decision making in the immune system

The immune system has long been attributed cognitive capacities such as "recognition" of pathogenic agents; "memory" of previous infections; "regulation" of a cavalry of detector and effector cells; and "adaptation" to a changing environment and evolving threats. Ostensibly, in preventing disease the immune system must be capable of discriminating states of pathology in the organism; identifying causal agents or ``pathogens''; and correctly deploying lethal effector mechanisms. What is more, these behaviours must be learnt insomuch as the paternal genes cannot encode the pathogenic environment of the child. Insights into the mechanisms underlying these phenomena are of interest, not only to immunologists, but to computer scientists pushing the envelope of machine autonomy. This thesis approaches these phenomena from the perspective that immunological processes are inherently inferential processes. By considering the immune system as a statistical decision maker, we attempt to build a bridge between the traditionally distinct fields of biological modelling and statistical modelling. Through a mixture of novel theoretical and empirical analysis we assert the efficacy of competitive exclusion as a general principle that benefits both. For the immunologist, the statistical modelling perspective allows us to better determine that which is phenomenologically sufficient from the mass of observational data, providing quantitative insight that may offer relief from existing dichotomies. For the computer scientist, the biological modelling perspective results in a theoretically transparent and empirically effective numerical method that is able to finesse the trade-off between myopic greediness and intractability in domains such as sparse approximation, continuous learning and boosting weak heuristics. Together, we offer this as a modern reformulation of the interface between computer science and immunology, established in the seminal work of Perelson and collaborators, over 20 years ago.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

An Immuno Inspired Approach To Generate White Noise

In this paper a new method to generate white noise is proposed. This method is based on viewing the white noise generation as an optimization problem and solving this problem with an immuno inspired algorithm. The white noise approximation obtained with the proposed method is nearer the ideal white noise than a series generated with a known pseudo random generator. The signal obtained with the new method was also applyed to discrete time series state space realization problem implying on results improvement. © 2011 IEEE.742749Faurre, P.L., Stochastic realization algorithms (1976) System Identification: Advances and Case Studies, 128, pp. 1-25Gevers, M.R., Kailath, T., An innovations approach to least squares estimation part vi - Discrete-time innovations representations and recursive estimation (1973) IEEE Transactions on Automatic Control, 18, pp. 588-600. , DecemberKailath, T., Geesey, R.A., An innovations approach to least squares estimation part iv - Recursive estimation given lumped covariance functions (1971) IEEE Transactions on Automatic Control, 16, pp. 720-727. , DecemberAnderson, B.D.O., Moore, J.B., Loo, S.G., Spectral factorization of time varying covariance functions (1969) IEEE Transactions on Information Theory, 15, pp. 550-557. , SeptemberKalman, R.E., A new approach to linear filtering and prediction problems (1960) Transactions of the ASME-Journal of Basic Engineering, (82), pp. 35-45Aoki, M., (1987) State Space Modeling of Time Series, , Springer-VerlagHo, B.L., Kalman, R.E., Effective construction of linear state-variable models from input-output functions (1966) Regelungstechnikzeitschrift für Steuern, Regeln und Automatisieren, 14 (12), pp. 545-548Tobar, J., Bottura, C.P., Giesbrecht, M., Computational modeling of multivariable non-stationary time series in the state space by the aoki var algorithm Proceedings of the 2010 World Congress on Engineering, 2010Barreto, G., (2002) Modelagem Computacional Distribuída e Paralela de Sistemas e de Séries Temporais Multivariáveis No Espaço de Estado, , PhD thesis, UnicampBarreto, G., Bottura, C.P., Revisitando os fundamentos de identificação multivariável no espaço de estados ii - Idéias básicas para o método de subespaços Proceedings of 2nd DINCON, August 2003Bottura, C.P., Barreto, G., Revisitando os fundamentos de identificação multivariável no espaço de estados i - Realização de estado e operador de hankel Proceedings of 2nd DINCON, August 2003Katayama, T., (2005) Subspace Methods for System Identification: A Realization Approach, , Leipzig: Springer VerlagGiesbrecht, M., Bottura, C.P., An immuno-inspired approach to find the steady state solution of riccati equations not solvable by schur method (2010) Proceedings of the 2010 IEEE World Congress on Computational Inteligence, pp. 1-8. , JulyDe Castro, L.N., Timmis, J., (2002) Artificial Immune Systems - A New Computational Intelligence Approach, , Springer VerlagDe Castro, L.N., Von Zuben, F.J., The clonal selection algorithm with engineering applications (2000) Workshop Proceedings of the GECCO 2000, pp. 36-37. , JulyDe Castro, L.N., Von Zuben, F.J., Learning and optimization using the clonal selection principle (2002) IEEE Transactions on Evolutionary Computation, 6, pp. 239-251. , JuneCutello, V., Nicosia, G., An immunological approach to combinatorial optimization problems (2002) Advances in Artifical Intelligence IBERAMIA, 252, pp. 361-370Cutello, V., Narizi, G., Nicosia, G., Pavone, M., Real coded clonal selection algortithm for global numerical optimization using a new inversely proportional hypermutation operator (2006) 21st Annual ACM Symposium on Applied Computing, SAC 2006, pp. 950-954Cutello, V., Narzisi, G., Nicosia, G., Pavone, M., Clonal selection algorithms: A comparative case study usign effective mutation potentials (2005) 4th Intl. Conference on Artificial Immune Systems ICARIS 2005, pp. 13-2

Immuno Inspired Approaches To Model Discrete Time Series At State Space

In this paper a new method for discrete time series state space modeling is proposed. The method is based on viewing the modeling problem as a constrained optimization problem. To solve the constrained optimization problem three imuno-inspired algorithms are proposed. An example is proposed to compare algorithms performance. Although the developed algorithms are dedicated to an specific problem, some ideas proposed in this paper can be used to solve any constrained optimization problem with immuno inspired algorithms. © 2011 IEEE.750756Faurre, P.L., Stochastic realization algorithms (1976) System Identification: Advances and Case Studies, 128, pp. 1-25Gevers, M.R., Kailath, T., An innovations approach to least squares estimation part vi - Discrete-time innovations representations and recursive estimation (1973) IEEE Transactions on Automatic Control, 18, pp. 588-600. , DecemberKailath, T., Geesey, R.A., An innovations approach to least squares estimation part iv - Recursive estimation given lumped covariance functions (1971) IEEE Transactions on Automatic Control, 16, pp. 720-727. , DecemberAnderson, B.D.O., Moore, J.B., Loo, S.G., Spectral factorization of time varying covariance functions (1969) IEEE Transactions on Information Theory, 15, pp. 550-557. , SeptemberKalman, R.E., A new approach to linear filtering and prediction problems (1960) Transactions of the ASME-Journal of Basic Engineering, (82), pp. 35-45Aoki, M., (1987) State Space Modeling of Time Series, , Springer-VerlagHo, B.L., Kalman, R.E., Effective construction of linear state-variable models from input-output functions (1966) Regelungstechnik - Zeitschrift für Steuern, Regeln und Automatisieren, 14 (12), pp. 545-548Tobar, J., Bottura, C.P., Giesbrecht, M., Computational modeling of multivariable non-stationary time series in the state space by the aoki var algorithm Proceedings of the 2010 World Congress on Engineering, 2010Barreto, G., (2002) Modelagem Computacional Distribuída e Paralela de Sistemas e de Séries Temporais Multivariáveis No Espaço de Estado, , PhD thesis, UnicampBarreto, G., Bottura, C.P., Revisitando os fundamentos de identificação multivariável no espaço de estados ii - Idéias básicas para o método de subespaços Proceedings of 2nd DINCON, August 2003Bottura, C.P., Barreto, G., Revisitando os fundamentos de identificação multivariável no espaço de estados i - Realização de estado e operador de hankel Proceedings of 2nd DINCON, August 2003Katayama, T., (2005) Subspace Methods for System Identification: A Realization Approach, , Leipzig: Springer VerlagGiesbrecht, M., Bottura, C.P., An immuno-inspired approach to find the steady state solution of riccati equations not solvable by schur method (2010) Proceedings of the 2010 IEEE World Congress on Computational Inteligence, pp. 1-8. , JulyDe Castro, L.N., Timmis, J., (2002) Artificial Immune Systems - A New Computational Intelligence Approach, , Springer VerlagDe Castro, L.N., Von Zuben, F.J., The clonal selection algorithm with engineering applications (2000) Workshop Proceedings of the GECCO 2000, pp. 36-37. , JulyDe Castro, L.N., Von Zuben, F.J., Learning and optimization using the clonal selection principle (2002) IEEE Transactions on Evolutionary Computation, 6, pp. 239-251. , JuneCutello, V., Nicosia, G., An immunological approach to combinatorial optimization problems (2002) Advances in Artifical Intelligence IBERAMIA, 2527, pp. 361-370Cutello, V., Narizi, G., Nicosia, G., Pavone, M., Real coded clonal selection algortithm for global numerical optimization using a new inversely proportional hypermutation operator (2006) 21st Annual ACM Symposium on Applied Computing, SAC 2006, pp. 950-954Cutello, V., Narzisi, G., Nicosia, G., Pavone, M., Clonal selection algorithms: A comparative case study usign effective mutation potentials (2005) 4th Intl. Conference on Artificial Immune Systems ICARIS 2005, pp. 13-28Giesbrecht, M., Bottura, C.P., Uma proposta imuno-inspirada para a solução algébrica da equação de riccati no problema de identificação de séries temporais no espaço de estado Anais Do XVIII Congresso Brasileiro de Automática, Setembro 201