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Erschienen in:
Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Efficient Search of Relevant Structures in Complex Systems

verfasst von : Laura Sani, Michele Amoretti, Emilio Vicari, Monica Mordonini, Riccardo Pecori, Andrea Roli, Marco Villani, Stefano Cagnoni, Roberto Serra

Erschienen in: AI*IA 2016 Advances in Artificial Intelligence

Verlag: Springer International Publishing

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Abstract

In a previous work, Villani et al. introduced a method to identify candidate emergent dynamical structures in complex systems. Such a method detects subsets (clusters) of the system elements which behave in a coherent and coordinated way while loosely interacting with the remainder of the system. Such clusters are assessed in terms of an index that can be associated to each subset, called Dynamical Cluster Index (DCI). When large systems are analyzed, the “curse of dimensionality” makes it impossible to compute the DCI for every possible cluster, even using massively parallel hardware such as GPUs.
In this paper, we propose an efficient metaheuristic for searching relevant dynamical structures, which hybridizes an evolutionary algorithm with local search and obtains results comparable to an exhaustive search in a much shorter time. The effectiveness of the method we propose has been evaluated on a set of Boolean models of real-world systems.

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Vorheriges Kapitel Optimized Word-Size Time Series Representation Method Using a Genetic Algorithm with a Flexible Encoding Scheme
Nächstes Kapitel Flat and Hierarchical Classifiers for Detecting Emotion in Tweets
Fußnoten
2
In one of the 10 runs, HyReSS failed to detect 1 of the first 50 RSs detected by the exhaustive search.
 
Literatur
1.
Prokopenko, M., Boschetti, F., Ryan, A.J.: An information-theoretic primer on complexity, self-organization, and emergence. Complexity 15(1), 11–28 (2009)MathSciNetCrossRef
2.
Villani, M., Filisetti, A., Benedettini, S., Roli, A., Lane, D., Serra, R.: The detection of intermediate-level emergent structures and patterns. In: Miglino, O., et al. (eds.) Advances in Artificial Life, ECAL 2013, pp. 372–378. The MIT Press, Cambridge (2013)CrossRef
3.
Gershenson, C., Fernandez, N.: Complexity and information: measuring emergence, self-organization, and homeostasis at multiple scales. Complex 18(2), 29–44 (2012)CrossRef
4.
Amoretti, M., Gershenson, C.: Measuring the complexity of adaptive peer-to-peer systems. Peer-to-Peer Netw. Appl. 9(6), 1031–1046 (2016)
5.
Febres, G., Jaff, K.: Calculating entropy at different scales among diverse communication systems. Complexity 21(S1), 330–353 (2016)CrossRef
6.
Marull, J., Font, C., Padr, R., Tello, E., Panazzolo, A.: Energy landscape integrated analysis: a proposal for measuring complexity in internal agroecosystem processes (barcelona metropolitan region, 18602000). Ecol. Indic. 66, 30–46 (2016)CrossRef
7.
Filisetti, A., Villani, M., Roli, A., Fiorucci, M., Serra, R.: Exploring the organisation of complex systems through the dynamical interactions among their relevant subsets. In: Andrews, P. et al., (eds.) Proceedings of the European Conference on Artificial Life 2015, ECAL 2015, pp. 286–293. The MIT Press (2015)
8.
Villani, M., Roli, A., Filisetti, A., Fiorucci, M., Poli, I., Serra, R.: The search for candidate relevant subsets of variables in complex systems. Artif. Life 21(4), 412–431 (2015)CrossRef
9.
Tononi, G., McIntosh, A., Russel, D., Edelman, G.: Functional clustering: identifying strongly interactive brain regions in neuroimaging data. Neuroimage 7, 133–149 (1998)CrossRef
10.
Tononi, G., Sporns, O., Edelman, G.M.: A measure for brain complexity: relating functional segregation and integration in the nervous system. Proc. Natl. Acad. Sci. 91(11), 5033–5037 (1994)CrossRef
11.
Filisetti, A., Villani, M., Roli, A., Fiorucci, M., Poli, I., Serra, R.: On some properties of information theoretical measures for the study of complex systems. In: Pizzuti, C., Spezzano, G. (eds.) WIVACE 2014. CCIS, vol. 445, pp. 140–150. Springer, Heidelberg (2014)
12.
Chen, X., Ong, Y.S., Lim, M.H., Tan, K.C.: A multi-facet survey on memetic computation. IEEE Trans. Evol. Comput. 15(5), 591–607 (2011)CrossRef
13.
Hu, X.M., Zhang, J., Yu, Y., Chung, H.S.H., Li, Y.L., Shi, Y.H., Luo, X.N.: Hybrid genetic algorithm using a forward encoding scheme for lifetime maximization of wireless sensor networks. IEEE Trans. Evol. Comput. 14(5), 766–781 (2010)CrossRef
14.
Behbahani, S., de Silva, C.W.: Niching genetic scheme with bond graphs for topology and parameter optimization of a mechatronic system. IEEE/ASME Trans. Mechatron. 19(1), 269–277 (2014)CrossRef
15.
Chang, D., Zhao, Y., Zheng, C.: A real-valued quantum genetic niching clustering algorithm and its application to color image segmentation. In: International Conference on Intelligent Computation and Bio-Medical Instrumentation (ICBMI), pp. 144–147, December 2011
16.
Pereira, M.W., Neto, G.S., Roisenberg, M.: A topological niching covariance matrix adaptation for multimodal optimization. In: IEEE Congress on Evolutionary Computation (CEC), pp. 2562–2569, July 2014
17.
Goldberg, D.E., Richardson, J.: Genetic algorithms with sharing for multimodal function optimization. In: Proceedings of the Second International Conference on Genetic Algorithms on Genetic Algorithms and Their Application, pp. 41–49. L. Erlbaum Associates Inc., Hillsdale (1987)
18.
Beasley, D., Bull, D.R., Martin, R.R.: A sequential niche technique for multimodal function optimization. Evol. Comput. 1(2), 101–125 (1993)CrossRef
19.
Manner, R., Mahfoud, S., Mahfoud, S.W.: Crowding and preselection revisited. In: Parallel Problem Solving From Nature, North-Holland, pp. 27–36 (1992)
20.
Harik, G.R.: Finding multimodal solutions using restricted tournament selection. In: Proceedings of the 6th International Conference on Genetic Algorithms, pp. 24–31. Morgan Kaufmann Publishers Inc., San Francisco (1995)
21.
Lacy, S.E., Lones, M.A., Smith, S.L.: Forming classifier ensembles with multimodal evolutionary algorithms. In: IEEE Congress on Evolutionary Computation (CEC), pp. 723–729 (2015)
22.
Will, A., Bustos, J., Bocco, M., Gotay, J., Lamelas, C.: On the use of niching genetic algorithms for variable selection in solar radiation estimation. Renew. Energy 50, 168–176 (2013)CrossRef
23.
Yannibelli, V., Amandi, A.: A deterministic crowding evolutionary algorithm to form learning teams in a collaborative learning context. Expert Syst. Appl. 39(10), 8584–8592 (2012)CrossRef
24.
Villani, M., Barbieri, A., Serra, R.: A dynamical model of genetic networks for cell differentiation. PloS one 6(3), e17703 (2011)CrossRef
25.
Shalizi, C.R., Camperi, M.F., Klinkner, K.L.: Discovering Functional Communities in Dynamical Networks. In: Airoldi, E., Blei, D.M., Fienberg, S.E., Goldenberg, A., Xing, E.P., Zheng, A.X. (eds.) ICML 2006. LNCS, vol. 4503, pp. 140–157. Springer, Heidelberg (2007)CrossRef
26.
Sporns, O., Tononi, G., Edelman, G.: Theoretical neuroanatomy: Relating anatomical and functional connectivity in graphs and cortical connection matrices. Cereb. Cortex 10(2), 127–141 (2000)CrossRef
27.
Cover, T., Thomas, A.: Elements of Information Theory, 2nd edn. Wiley-Interscience, New York (2006)MATH
28.
Villani, M., Carra, P., Roli, A., Filisetti, A., Serra, R.: On the robustness of the detection of relevant sets in complex dynamical systems. In: Rossi, F., Mavelli, F., Stano, P., Caivano, D. (eds.) WIVACE 2015. CCIS, vol. 587, pp. 15–28. Springer, Heidelberg (2016)CrossRef
29.
Anzoise, V., Sardo, S.: Dynamic systems and the role of evaluation: the case of the green communities project. Eval. Program Plan. 54, 162–172 (2016)CrossRef
Metadaten
Titel
Efficient Search of Relevant Structures in Complex Systems
verfasst von
Laura Sani
Michele Amoretti
Emilio Vicari
Monica Mordonini
Riccardo Pecori
Andrea Roli
Marco Villani
Stefano Cagnoni
Roberto Serra
Copyright-Jahr
2016
Buch
AI*IA 2016 Advances in Artificial Intelligence

Print ISBN: 978-3-319-49129-5

Electronic ISBN: 978-3-319-49130-1

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-49130-1_4

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