]> BibSonomy publications for /author/Fonseca http://www.bibsonomy.org/burst/author/Fonseca BibSonomy BuRST Feed for /author/Fonseca 2008-07-27T02:24:26+02:00 Online data mining services for dynamic spatial databases I: system architecture and client applications http://www.bibsonomy.org/bibtex/2f024b2f6eb7d77cb479965369bfb6a0c/lteixeira lteixeira 2008-07-13T18:40:32+02:00 imported
Online data mining services for dynamic spatial databases I: system architecture and client applications
Manuel Costa and Luis Teixeira and Alvaro Barbosa and Jorge Cardoso and Vasco Carvalho and Ines Sousa and Ivan Franco and Alexandra Fonseca and Diana Henriques and Paulo Rosa II International Conference and Exhibition on Geographic Information (2005)
to imported by lteixeira on 2008-07-13 18:40:32 ]]> II International Conference and Exhibition on Geographic InformationOnline data mining services for dynamic spatial databases I: system architecture and client applications2005imported 2008-07-13 18:40:32.0 Agrafo: a visual interface for grouping and browsing digital photos. dblphttp://www.bibsonomy.org/bibtex/248b3523fd9149499caf2faedf8736271/dblp dblp 2008-07-13T00:00:00+02:00 dblp
Agrafo: a visual interface for grouping and browsing digital photos.
João Mota and Manuel J. Fonseca and Daniel Gonçalves and Joaquim A. Jorge AVI 494-495 (2008)
to dblp by dblp on 2008-07-13 00:00:00 ]]> AVIconf/avi/2008494-495Agrafo: a visual interface for grouping and browsing digital photos.2008dblp 2008-07-13 00:00:00.0 Structural and electronic properties of bent carbon nanotubes http://www.bibsonomy.org/bibtex/222f57d8891ed85666280c1c1fca6630c/sansiro sansiro 2008-07-07T09:08:07+02:00 imported
Structural and electronic properties of bent carbon nanotubes
P. Lambin and A. Fonseca and JP Vigneron and JB Nagy and AA Lucas Chem. Phys. Lett. 245 85--89 (1995)
to imported by sansiro on 2008-07-07 09:08:07 ]]> Chem. Phys. Lett.185--89{Structural and electronic properties of bent carbon nanotubes}2451995imported 2008-07-07 09:08:07.0 A Statistical Comparison of Grammatical Evolution Strategies in the Domain of Human Genetics http://www.bibsonomy.org/bibtex/27b429de4b4cccfb3906b92ded3dd51db/brazovayeye brazovayeye 2008-06-19T17:46:40+02:00 PSO, algorithms, evolution genetic grammatical programming,
A Statistical Comparison of Grammatical Evolution Strategies in the Domain of Human Genetics
Bill C. White and Joshua C. Gilbert and David M. Reif and Jason H. Moore Proceedings of the 2005 IEEE Congress on Evolutionary Computation 491--497 (2005)
to PSO, algorithms, evolution genetic grammatical programming, by brazovayeye on 2008-06-19 17:46:40 ]]> Edinburgh, UKProceedings of the 2005 IEEE Congress on Evolutionary Computation2-5 September491--497A Statistical Comparison of Grammatical Evolution Strategies in the Domain of Human Genetics12005PSO, algorithms, evolution genetic grammatical programming, 2008-06-19 17:46:40.0Detecting and characterising genetic predictors of human disease susceptibility is an important goal in human genetics. New chip-based technologies are available that facilitate the measurement of thousands of DNA sequence variations across the human genome. Biologically-inspired stochastic search algorithms are expected to play an important role in the analysis of these high-dimensional datasets. We simulated datasets with up to 6000 attributes using two different genetic models and statistically compared the performance of grammatical evolution, grammatical swarm, and random search for building symbolic discriminant functions. We found no statistical difference among search algorithms within this specific domain. Tree Swarm Optimization: An Approach to PSO-based Tree Discovery http://www.bibsonomy.org/bibtex/24cd00ffec922b9b2e9450a1da030bea5/brazovayeye brazovayeye 2008-06-19T17:46:40+02:00 PSO, TSO algorithms, genetic programming,
Tree Swarm Optimization: An Approach to PSO-based Tree Discovery
Christian Veenhuis and Mario K{\"o}ppen and J{\"o}rg Kr{\"u}ger and Bertram Nickolay Proceedings of the 2005 IEEE Congress on Evolutionary Computation 1238--1245 (2005)
to PSO, TSO algorithms, genetic programming, by brazovayeye on 2008-06-19 17:46:40 ]]> Edinburgh, UKProceedings of the 2005 IEEE Congress on Evolutionary Computation2-5 September1238--1245Tree Swarm Optimization: An Approach to {PSO}-based Tree Discovery22005PSO, TSO algorithms, genetic programming, 2008-06-19 17:46:40.0In recent years a swarm-based optimisation methodology called Particle Swarm Optimisation (PSO) has developed. PSO is highly explorative and primarily used in function optimisation. proposes a swarm-based learning algorithm based on PSO which is able to discover trees in tree spaces. Particles are flying through a tree space forming flocks around peaks of a fitness function. Because it inherits the explorative property of PSO, it needs only few evaluations to find suitable trees. Classification of Proteomic Kinetic Patterns using Supervised Genetic Programming http://www.bibsonomy.org/bibtex/2fd33ba9928abb4d3ce9fa1e123bc98d1/brazovayeye brazovayeye 2008-06-19T17:46:40+02:00 SWICZ, algorithms, coelicolor genetic programming, s.
Classification of Proteomic Kinetic Patterns using Supervised Genetic Programming
Cuong To and Jiri Vohradsky Proceedings of the 2005 IEEE Congress on Evolutionary Computation 1823--1830 (2005)
to SWICZ, algorithms, coelicolor genetic programming, s. by brazovayeye on 2008-06-19 17:46:40 ]]> Edinburgh, UKProceedings of the 2005 IEEE Congress on Evolutionary Computation2-5 September1823--1830Classification of Proteomic Kinetic Patterns using Supervised Genetic Programming22005SWICZ, algorithms, coelicolor genetic programming, s. 2008-06-19 17:46:40.0The rapidly emerging field of quantitative proteomics has established itself as a credible approach for understanding of the biology of whole organisms. Classification of proteins according to the level of their expression during a particular process allows discovering causal relationships among genes and proteins involved in the process. we present a supervised method of classification of proteomic kinetic patterns based on genetic programming allowing for extraction of user defined patterns from a database of kinetic profiles. The method combines robustness of genetic programming algorithm with the flexibility given by user interaction. Comparing Tree Depth Limits and Resource-Limited GP http://www.bibsonomy.org/bibtex/2febef2f2d14240ea11dd30be3a9d7014/brazovayeye brazovayeye 2008-06-19T17:46:40+02:00 algorithms, genetic programming
Comparing Tree Depth Limits and Resource-Limited GP
Sara Silva and Ernesto Costa Proceedings of the 2005 IEEE Congress on Evolutionary Computation 920--927 (2005)
to algorithms, genetic programming by brazovayeye on 2008-06-19 17:46:40 ]]> Edinburgh, UKProceedings of the 2005 IEEE Congress on Evolutionary Computation2-5 September920--927Comparing Tree Depth Limits and Resource-Limited {GP}12005algorithms, genetic programming 2008-06-19 17:46:40.0we compare two different approaches for controlling bloat in Genetic Programming, tree depth limits and resource-limited GP. Tree depth limits operate at the individual level, avoiding excessive code growth by imposing a maximum depth to each individual. Resource-limited GP is a new technique that operates at the population level, limiting the total amount of resources the entire population can use. We compare their dynamics and performance on three problems: Symbolic Regression, Even Parity, and Artificial Ant. The results suggest that resource-limited GP is superior to tree depth limits, but we question this superiority and discuss possible ways of combining the strengths of both approaches, to further improve the results. Nonlinear identification of aircraft gas-turbine dynamics http://www.bibsonomy.org/bibtex/249b27e13b0904858d472a6cc16688eba/brazovayeye brazovayeye 2008-06-19T17:46:40+02:00 algorithms, genetic programming
Nonlinear identification of aircraft gas-turbine dynamics
A. E. Ruano and P. J. Fleming and C. Teixeira and K. Rodriguez-Vazquez and C. M. Fonseca Neurocomputing 55 551--579 (2003)
to algorithms, genetic programming by brazovayeye on 2008-06-19 17:46:40 ]]> Neurocomputing3-4551--579Nonlinear identification of aircraft gas-turbine dynamics552003algorithms, genetic programming 2008-06-19 17:46:40.0Identification results for the shaft-speed dynamics of an aircraft gas turbine, under normal operation, are presented. As it has been found that the dynamics vary with the operating point, nonlinear models are employed. Two different approaches are considered: NARX models, and neural network models, namely multilayer perceptrons, radial basis function networks and B-spline networks. A special attention is given to genetic programming, in a multiobjective fashion, to determine the structure of NARMAX and B-spline models. Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming http://www.bibsonomy.org/bibtex/21f8125a7b3eb362064bdea79168d6294/brazovayeye brazovayeye 2008-06-19T17:46:40+02:00 algorithms, genetic programming
Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming
Katya Rodriguez-Vazquez and Carlos M. Fonseca and Peter J. Fleming IEEE Transactions on Systems, Man and Cybernetics, Part A 34 531--545 (2004)
to algorithms, genetic programming by brazovayeye on 2008-06-19 17:46:40 ]]> IEEE Transactions on Systems, Man and Cybernetics, Part AJuly4531--545Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming342004algorithms, genetic programming 2008-06-19 17:46:40.0A method for identifying the structure of nonlinear polynomial dynamic models is presented. This approach uses an evolutionary algorithm, genetic programming, in a multiobjective fashion to generate global models which describe the dynamic behaviour of the nonlinear system under investigation. The validation stage of system identification is simultaneously evaluated using the multiobjective tool, in order to direct the identification process to a set of global models of the system. Multiobjective Genetic Programming: A Nonlinear System Identification Application http://www.bibsonomy.org/bibtex/2bdc04441a507518f6e847b031d7574a9/brazovayeye brazovayeye 2008-06-19T17:46:40+02:00 algorithms, genetic programming
Multiobjective Genetic Programming: A Nonlinear System Identification Application
Katya Rodriguez-Vazquez and Carlos M. Fonseca and Peter J. Fleming Late Breaking Papers at the 1997 Genetic Programming Conference 207--212 (1997)
to algorithms, genetic programming by brazovayeye on 2008-06-19 17:46:40 ]]> Stanford University, CA, USALate Breaking Papers at the 1997 Genetic Programming Conference13--16 July207--212Multiobjective Genetic Programming: {A} Nonlinear System Identification Application1997algorithms, genetic programming 2008-06-19 17:46:40.0