659 research outputs found
Towards Improving Clustering Ants: An Adaptive Ant Clustering Algorithm
Among the many bio-inspired techniques, ant-based clustering algorithms have received special attention from the community over the past few years for two main reasons. First, they are particularly suitable to perform exploratory data analysis and, second, they still require much investigation to improve performance, stability, convergence, and other key features that would make such algorithms mature tools for diverse applications. Under this perspective, this paper proposes both a progressive vision scheme and pheromone heuristics for the standard ant-clustering algorithm, together with a cooling schedule that improves its convergence properties. The proposed algorithm is evaluated in a number of well-known benchmark data sets, as well as in a real-world bio informatics dataset. The achieved results are compared to those obtained by the standard ant clustering algorithm, showing that significant improvements are obtained by means of the proposed modifications. As an additional contribution, this work also provides a brief review of ant-based clustering algorithms.292143154Abraham, A., Ramos, V., Web usage mining using artificial ant colony clustering and genetic programming (2003) Proc. of the Congress on Evolutionary Computation (CEC 2003), pp. 1384-1391. , Canberra, IEEE PressBezdek, J.C., (1981) Pattern Recognition with Fuzzy Objective Function Algorithm, , Plenum PressBonabeau, E., Dorigo, M., Théraulaz, G., (1999) Swarm Intelligence from Natural to Artificial Systems, , Oxford University PressCamazine, S., Deneubourg, J.-L., Franks, N.R., Sneyd, J., Theraulaz, G., Bonabeau, E., (2001) Self-organization in Biological Systems, , Princeton University PressDe Castro, L.N., Von Zuben, F.J., (2004) Recent Developments in Biologically Inspired Computing, , Idea Group IncDeneubourg, J.L., Goss, S., Sendova-Franks, N.A., Detrain, C., Chrétien, L., The dynamics of collective sorting: Robot-like ant and ant-like robot (1991) Simulation of Adaptive Behavior: from Animals to Animats, pp. 356-365. , J. A. Meyer and S. W. Wilson (eds.). MIT Press/Bradford BooksEveritt, B.S., Landau, S., Leese, M., (2001) Cluster Analysis, , Arnold Publishers, LondonGutowitz, H., Complexity-seeking ants (1993) Proceedings of the Third European Conference on Artificial LifeHandl, J., Knowles, J., Dorigo, M., On the performance of ant-based clustering (2003) Proc. of the 3rd International Conference on Hybrid Intelligent Systems, Design and Application of Hybrid Intelligent Systems, pp. 204-213. , IOS PressHandl, J., Meyer, B., Improved ant-based clustering and sorting in a document retrieval interface (2002) Lecture Notes in Computer Science, 2439, pp. 913-923. , J.J. Merelo, J.L.F. Villacañas, H.G. Beyer, P. Adamis Eds.: Proceedings of the PPSN VII - 7th Int. Conf. on Parallel Problem Solving from Nature, Granada, Spain, Springer-Verlag, BerlinKanade, P., Hall, L.O., Fuzzy ants as a clustering concept (2003) Proc. of the 22nd International Conference of the North American Fuzzy Information Processing Society (NAFIPS), pp. 227-232Kaufman, L., Rousseeuw, P.J., (1990) Finding Groups in Data - An Introduction to Cluster Analysis, Wiley Series in Probability and Mathematical Statistics, , John Wiley & Sons IncKeim, D.A., (2002) Information Visualization and Visual Data Mining: IEEE Transactions on Visuali Zation and Computer Graphics, 7 (1), pp. 100-107Kennedy, J., Eberhart, R., Shi, Y., (2001) Swarm Intelligence, , Morgan Kaufmann PublishersLabroche, N., Monmarché, N., Venturini, G., A new clustering algorithm based on the chemical recognition system of ants (2002) Proc. of the 15th European Conference on Artificial Intelligence, pp. 345-349. , France, IOS PressLumer, E.D., Faieta, B., Diversity and adaptation in populations of clustering ants (1994) Proceedings of the Third International Conference on the Simulation of Adaptive Behavior: from Animals to Animats, 3, pp. 499-508. , MIT PressMonmarché, N., Slimane, M., Venturini, G., On improving clustering in numerical databases with artificial ants. Advances in artificial life (1999) Lecture Notes in Computer Science, 1674, pp. 626-635. , D. Floreano, J.D. Nicoud, and F. Mondala Eds., Springer-Verlag, BerlinPaton, R., (1994) Computing with Biological Metaphors, , Chapman & HallRamos, V., Merelo, J.J., Self-organized stigmergic document maps: Environment as a mechanism for context learning (2002) AEB'2002, First Spanish Conference on Evolutionary and BioInspired Algorithms, pp. 284-293. , E. Alba, F. Herrera, J.J. Merelo et al. Eds., SpainRamos, V., Muge, F., Pina, P., Self-organized data and image retrieval as a consequence of inter-dynamic synergistic relationships in artificial ant colonies (2002) Soft-Computing Systems - Design, Management and Applications, Frontiers in Artificial Intelligence and Applications, 87, pp. 500-509. , J. Ruiz-del-Solar, A. Abrahan and M. Köppen Eds. IOS Press, AmsterdamRitter, H., Kohonen, T., Self-organizing semantic maps (1989) Biol. Cybern., 61, pp. 241-254Sherafat, V., De Castro, L.N., Hruschka, E.R., TermitAnt: An ant clustering algorithm improved by ideas from termite colonies (2004) Lecture Notes in Computer Science, 3316, pp. 1088-1093. , Proc. of ICONIP 2004, Special Session on Ant Colony and Multi-Agent SystemsSherafat, V., De Castro, L.N., Hruschka, E.R., The influence of pheromone and adaptive vision on the standard ant clustering algorithm (2004) Recent Developments in Biologically Inspired Computing, pp. 207-234. , L. N. de Castro and F. J. Von Zuben, Chapter IX. Idea Group IncVizine, A.L., De Castro, L.N., Gudwin, R.R., Text document classification using swarm intelligence (2005) Proc. of KIMAS 2005, , CD ROMYeung, K.Y., Medvedovic, M., Bumgarner, R.E., Clustering gene-expression data with repeated measurements (2003) Genome Biology, 4 (5), pp. R34. , articl
An Artificial Immune System for Misbehavior Detection in Mobile Ad-Hoc Networks with Virtual Thymus, Clustering, Danger Signal and Memory Detectors
In mobile ad-hoc networks, nodes act both as terminals and information relays, and participate in a common routing protocol, such as Dynamic Source Routing (DSR). The network is vulnerable to routing misbehavior, due to faulty or malicious nodes. Misbehavior detection systems aim at removing this vulnerability. For this purpose, we use an Artificial Immune System (AIS), a system inspired by the human immune system (HIS). Our goal is to build a system that, like its natural counterpart, automatically learns and detects new misbehavior. In this paper we build on our previous work and investigate the use of four concepts: (1
Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials
Solving Optimization Problems by the Public Goods Game
This document is the Accepted Manuscript version of the following article: Marco Alberto Javarone, ‘Solving optimization problems by the public goods game’, The European Physical Journal B, 90:17, September 2017. Under embargo. Embargo end date: 18 September 2018. The final, published version is available online at doi: https://doi.org/10.1140/epjb/e2017-80346-6. Published by Springer Berlin Heidelberg.We introduce a method based on the Public Goods Game for solving optimization tasks. In particular, we focus on the Traveling Salesman Problem, i.e. a NP-hard problem whose search space exponentially grows increasing the number of cities. The proposed method considers a population whose agents are provided with a random solution to the given problem. In doing so, agents interact by playing the Public Goods Game using the fitness of their solution as currency of the game. Notably, agents with better solutions provide higher contributions, while those with lower ones tend to imitate the solution of richer agents for increasing their fitness. Numerical simulations show that the proposed method allows to compute exact solutions, and suboptimal ones, in the considered search spaces. As result, beyond to propose a new heuristic for combinatorial optimization problems, our work aims to highlight the potentiality of evolutionary game theory beyond its current horizons.Peer reviewedFinal Accepted Versio
An Artificial Immune System-Inspired Multiobjective Evolutionary Algorithm with Application to the Detection of Distributed Computer Network Intrusions
Today\u27s signature-based intrusion detection systems are reactive in nature and storage-limited. Their operation depends upon catching an instance of an intrusion or virus and encoding it into a signature that is stored in its anomaly database, providing a window of vulnerability to computer systems during this time. Further, the maximum size of an Internet Protocol-based message requires the database to be huge in order to maintain possible signature combinations. In order to tighten this response cycle within storage constraints, this paper presents an innovative Artificial Immune System-inspired Multiobjective Evolutionary Algorithm. This distributed intrusion detection system (IDS) is intended to measure the vector of tradeoff solutions among detectors with regard to two independent objectives: best classification fitness and optimal hypervolume size. Our antibody detectors promiscuously monitor network traffic for exact and variant abnormal system events based on only the detector\u27s own data structure and the application domain truth set, responding heuristically. Applied to the MIT-DARPA 1999 insider intrusion detection data set, our software engineered algorithm correctly classifies normal and abnormal events at a high level which is directly attributed to a detector affinity threshold
A Randomized Real-Valued Negative Selection Algorithm
This paper presents a real-valued negative selection algorithm with good mathematical foundation that solves some of the drawbacks of our previous approach [11]. Specifically, it can produce a good estimate of the optimal number of detectors needed to cover the non-self space, and the maximization of the non-self coverage is done through an optimization algorithm with proven convergence properties. The proposed method is a randomized algorithm based on Monte Carlo methods. Experiments are performed to validate the assumptions made while designing the algorithm and to evaluate its performance. © Springer-Verlag Berlin Heidelberg 2003
Yield and mineral nutrition of soybean, maize, and Congo signal grass as affected by limestone and slag
Investigating a Hybrid Metaheuristic For Job Shop Rescheduling
Previous research has shown that artificial immune systems can be used to
produce robust schedules in a manufacturing environment. The main goal is to
develop building blocks (antibodies) of partial schedules that can be used to
construct backup solutions (antigens) when disturbances occur during
production. The building blocks are created based upon underpinning ideas from
artificial immune systems and evolved using a genetic algorithm (Phase I). Each
partial schedule (antibody) is assigned a fitness value and the best partial
schedules are selected to be converted into complete schedules (antigens). We
further investigate whether simulated annealing and the great deluge algorithm
can improve the results when hybridised with our artificial immune system
(Phase II). We use ten fixed solutions as our target and measure how well we
cover these specific scenarios
Search for a W' boson decaying to a bottom quark and a top quark in pp collisions at sqrt(s) = 7 TeV
Results are presented from a search for a W' boson using a dataset
corresponding to 5.0 inverse femtobarns of integrated luminosity collected
during 2011 by the CMS experiment at the LHC in pp collisions at sqrt(s)=7 TeV.
The W' boson is modeled as a heavy W boson, but different scenarios for the
couplings to fermions are considered, involving both left-handed and
right-handed chiral projections of the fermions, as well as an arbitrary
mixture of the two. The search is performed in the decay channel W' to t b,
leading to a final state signature with a single lepton (e, mu), missing
transverse energy, and jets, at least one of which is tagged as a b-jet. A W'
boson that couples to fermions with the same coupling constant as the W, but to
the right-handed rather than left-handed chiral projections, is excluded for
masses below 1.85 TeV at the 95% confidence level. For the first time using LHC
data, constraints on the W' gauge coupling for a set of left- and right-handed
coupling combinations have been placed. These results represent a significant
improvement over previously published limits.Comment: Submitted to Physics Letters B. Replaced with version publishe
Search for the standard model Higgs boson decaying into two photons in pp collisions at sqrt(s)=7 TeV
A search for a Higgs boson decaying into two photons is described. The
analysis is performed using a dataset recorded by the CMS experiment at the LHC
from pp collisions at a centre-of-mass energy of 7 TeV, which corresponds to an
integrated luminosity of 4.8 inverse femtobarns. Limits are set on the cross
section of the standard model Higgs boson decaying to two photons. The expected
exclusion limit at 95% confidence level is between 1.4 and 2.4 times the
standard model cross section in the mass range between 110 and 150 GeV. The
analysis of the data excludes, at 95% confidence level, the standard model
Higgs boson decaying into two photons in the mass range 128 to 132 GeV. The
largest excess of events above the expected standard model background is
observed for a Higgs boson mass hypothesis of 124 GeV with a local significance
of 3.1 sigma. The global significance of observing an excess with a local
significance greater than 3.1 sigma anywhere in the search range 110-150 GeV is
estimated to be 1.8 sigma. More data are required to ascertain the origin of
this excess.Comment: Submitted to Physics Letters
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